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\n  \n 2024\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Cambridge University Estates Building Energy Usage Archive.\n \n \n \n \n\n\n \n Langtry, M., & Choudhary, R.\n\n\n \n\n\n\n May 2024.\n \n\n\n\n
\n\n\n\n \n \n \"CambridgePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{langtry2024CambridgeUniversityEstates,\n  title = {Cambridge {{University Estates}} Building Energy Usage Archive},\n  author = {Langtry, Max and Choudhary, Ruchi},\n  year = {2024},\n  month = may,\n  doi = {10.5281/zenodo.10955332},\n  url = {https://github.com/EECi/Cambridge-Estates-Building-Energy-Archive},\n  version = {2.1}\n}\n\n
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\n \n\n \n \n \n \n \n \n Impact of Data for Forecasting on Performance of Model Predictive Control in Buildings with Smart Energy Storage.\n \n \n \n \n\n\n \n Langtry, M., Wichitwechkarn, V., Ward, R., Zhuang, C., Kreitmair, M. J., Makasis, N., Conti, Z. X., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings,114605. July 2024.\n \n\n\n\n
\n\n\n\n \n \n \"ImpactPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{LANGTRY2024114605,\n  title = {Impact of Data for Forecasting on Performance of Model Predictive Control in Buildings with Smart Energy Storage},\n  author = {Langtry, Max and Wichitwechkarn, Vijja and Ward, Rebecca and Zhuang, Chaoqun and Kreitmair, Monika J. and Makasis, Nikolas and Conti, Zack Xuereb and Choudhary, Ruchi},\n  year = {2024},\n  month = jul,\n  journal = {Energy and Buildings},\n  pages = {114605},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2024.114605},\n  url = {https://www.sciencedirect.com/science/article/pii/S0378778824007217},\n  abstract = {Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency are quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10},\n  keywords = {Building energy optimization,Data requirements,Energy storage,Historical data,Machine learning,Model predictive control (MPC),Time-series forecasting}\n}\n
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\n Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency are quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10\n
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\n \n\n \n \n \n \n \n \n The Challenges of Using Live-Streamed Data in a Predictive Digital Twin.\n \n \n \n \n\n\n \n Ward, R., Choudary, R., Jans-Singh, M., Roumpani, F., Lazauskas, T., Yong, M., Barlow, N., & Hauru, M.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 0(0): 1–22. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2023.2187463,\n  title = {The Challenges of Using Live-Streamed Data in a Predictive Digital Twin},\n  author = {Ward, Rebecca and Choudary, Ruchi and {Jans-Singh}, Melanie and Roumpani, Flora and Lazauskas, Tomas and Yong, May and Barlow, Nicholas and Hauru, Markus},\n  year = {2023},\n  journal = {Journal of Building Performance Simulation},\n  volume = {0},\n  number = {0},\n  pages = {1--22},\n  publisher = {Taylor \\& Francis},\n  doi = {10.1080/19401493.2023.2187463},\n  url = {https://doi.org/10.1080/19401493.2023.2187463}\n}\n\n
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\n \n\n \n \n \n \n \n \n Value of Information Analysis for Rationalising Information Gathering in Building Energy Analysis.\n \n \n \n \n\n\n \n Langtry, M., Zhuang, C., Ward, R., Makasis, N., Kreitmair, M. J., Xuereb Conti, Z., Di Francesco, D., & Choudhary, R.\n\n\n \n\n\n\n In Building Simulation 2023, volume 18, of Building Simulation, September 2023. IBPSA\n \n\n\n\n
\n\n\n\n \n \n \"ValuePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{langtry2023ValueInformationAnalysis,\n  title = {Value of {{Information Analysis}} for Rationalising Information Gathering in Building Energy Analysis},\n  booktitle = {Building {{Simulation}} 2023},\n  author = {Langtry, Max and Zhuang, Chaoqun and Ward, Rebecca and Makasis, Nikolas and Kreitmair, Monika J. and Xuereb Conti, Zack and Di Francesco, Domenic and Choudhary, Ruchi},\n  year = {2023},\n  month = sep,\n  series = {Building {{Simulation}}},\n  volume = {18},\n  publisher = {IBPSA},\n  doi = {10.26868/25222708.2023.1478},\n  url = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1478},\n  urldate = {2024-07-31},\n  abstract = {The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that without a principled method for determining the value of data, its collection cannot be prioritised. It demonstrates the use of Value of Information analysis (VoI), which is a Bayesian Decision Analysis framework, to provide such a methodology for quantifying the value of data collection in the context of building energy modelling and analysis. Three energy decision-making examples are presented: ventilation scheduling, heat pump maintenance scheduling, and ground source heat pump design. These examples illustrate the use of VoI to support decision-making on data collection.},\n  langid = {american},\n  file = {/Users/mal84/Zotero/storage/BDS2U4EY/Langtry et al. - 2023 - Value of Information Analysis for rationalising in.pdf}\n}\n\n
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\n The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that without a principled method for determining the value of data, its collection cannot be prioritised. It demonstrates the use of Value of Information analysis (VoI), which is a Bayesian Decision Analysis framework, to provide such a methodology for quantifying the value of data collection in the context of building energy modelling and analysis. Three energy decision-making examples are presented: ventilation scheduling, heat pump maintenance scheduling, and ground source heat pump design. These examples illustrate the use of VoI to support decision-making on data collection.\n
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\n \n\n \n \n \n \n \n \n System Effects in Identifying Risk-Optimal Data Requirements for Digital Twins of Structures.\n \n \n \n \n\n\n \n Di Francesco, D., Langtry, M., Duncan, A. B., & Dent, C.\n\n\n \n\n\n\n September 2023.\n \n\n\n\n
\n\n\n\n \n \n \"SystemPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@misc{difrancesco2023SystemEffectsIdentifying,\n  title = {System {{Effects}} in {{Identifying Risk-Optimal Data Requirements}} for {{Digital Twins}} of {{Structures}}},\n  author = {Di Francesco, Domenic and Langtry, Max and Duncan, Andrew B. and Dent, Chris},\n  year = {2023},\n  month = sep,\n  number = {arXiv:2309.07695},\n  eprint = {2309.07695},\n  primaryclass = {stat},\n  publisher = {arXiv},\n  doi = {10.48550/arXiv.2309.07695},\n  url = {http://arxiv.org/abs/2309.07695},\n  urldate = {2024-02-20},\n  abstract = {Structural Health Monitoring (SHM) technologies offer much promise to the risk management of the built environment, and they are therefore an active area of research. However, information regarding material properties, such as toughness and strength is instead measured in destructive lab tests. Similarly, the presence of geometrical anomalies is more commonly detected and sized by inspection. Therefore, a risk-optimal combination should be sought, acknowledging that different scenarios will be associated with different data requirements. Value of Information (VoI) analysis is an established statistical framework for quantifying the expected benefit of a prospective data collection activity. In this paper the expected value of various combinations of inspection, SHM and testing are quantified, in the context of supporting risk management of a location of stress concentration in a railway bridge. The Julia code for this analysis (probabilistic models and influence diagrams) is made available. The system-level results differ from a simple linear sum of marginal VoI estimates, i.e. the expected value of collecting data from SHM and inspection together is not equal to the expected value of SHM data plus the expected value of inspection data. In summary, system-level decision making, requires system-level models.},\n  archiveprefix = {arXiv},\n  keywords = {Statistics - Applications},\n  file = {/Users/mal84/Zotero/storage/NWWPGFJN/Di Francesco et al. - 2023 - System Effects in Identifying Risk-Optimal Data Re.pdf;/Users/mal84/Zotero/storage/IYU8KYXL/2309.html}\n}\n\n
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\n Structural Health Monitoring (SHM) technologies offer much promise to the risk management of the built environment, and they are therefore an active area of research. However, information regarding material properties, such as toughness and strength is instead measured in destructive lab tests. Similarly, the presence of geometrical anomalies is more commonly detected and sized by inspection. Therefore, a risk-optimal combination should be sought, acknowledging that different scenarios will be associated with different data requirements. Value of Information (VoI) analysis is an established statistical framework for quantifying the expected benefit of a prospective data collection activity. In this paper the expected value of various combinations of inspection, SHM and testing are quantified, in the context of supporting risk management of a location of stress concentration in a railway bridge. The Julia code for this analysis (probabilistic models and influence diagrams) is made available. The system-level results differ from a simple linear sum of marginal VoI estimates, i.e. the expected value of collecting data from SHM and inspection together is not equal to the expected value of SHM data plus the expected value of inspection data. In summary, system-level decision making, requires system-level models.\n
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\n \n\n \n \n \n \n \n \n A Physics-based Domain Adaptation Framework for Modelling and Forecasting Building Energy Systems.\n \n \n \n \n\n\n \n Conti, Z. X., Choudhary, R., & Magri, L.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@misc{https://doi.org/10.48550/arxiv.2208.09456,\n  title = {A {{Physics-based Domain Adaptation}} Framework for Modelling and Forecasting Building Energy Systems},\n  author = {Conti, Zack Xuereb and Choudhary, Ruchi and Magri, Luca},\n  year = {2022},\n  publisher = {arXiv},\n  doi = {10.48550/ARXIV.2208.09456},\n  url = {https://arxiv.org/abs/2208.09456},\n  copyright = {Creative Commons Attribution 4.0 International},\n  keywords = {FOS: Computer and information sciences,Machine Learning (cs.LG),Machine Learning (stat.ML)}\n}\n\n
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\n  \n 2021\n \n \n (15)\n \n \n
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\n \n\n \n \n \n \n \n \n A Study on the Transferability of Computational Models of Building Electricity Load Patterns across Climatic Zones.\n \n \n \n \n\n\n \n Ward, R., Wong, C. S. Y., Chong, A., Choudhary, R., & Ramasamy, S.\n\n\n \n\n\n\n Energy and Buildings, 237: 110826. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{ward_transferability_2021,\n  title = {A Study on the Transferability of Computational Models of Building Electricity Load Patterns across Climatic Zones},\n  author = {Ward, Rebecca and Wong, Cheryl Sze Yin and Chong, Adrian and Choudhary, Ruchi and Ramasamy, Savitha},\n  year = {2021},\n  journal = {Energy and Buildings},\n  volume = {237},\n  pages = {110826},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2021.110826},\n  url = {https://www.sciencedirect.com/science/article/pii/S0378778821001109},\n  abstract = {Significant reduction in energy demand from non-domestic buildings is required if greenhouse emission reduction targets are to be met worldwide. Increasing monitoring of electricity consumption generates a real opportunity for gaining an in-depth understanding of the nature of occupant-related internal loads and the connection between activity and demand. The stochastic nature of the demand is well-known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. This paper presents evidence that it is feasible to generate stochastic models of activity-related electricity demand based on monitored data. Two machine learning approaches are used to develop stochastic models of plug loads; an autoencoder (AE) and a Functional Data Analysis (FDA) model. Using data from two office buildings located in different countries, the transferability of models is explored by training the models on data from one building and using the trained models to predict demand for the other building. The results show that both models predict plug loads satisfactorily, with a good agreement with the mean demand and quantification of the uncertainty.},\n  keywords = {Autoencoder (AE),Electricity demand,Functional Data Analysis (FDA),Machine learning,Plug loads,Stochastic model,Transferability}\n}\n\n
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\n Significant reduction in energy demand from non-domestic buildings is required if greenhouse emission reduction targets are to be met worldwide. Increasing monitoring of electricity consumption generates a real opportunity for gaining an in-depth understanding of the nature of occupant-related internal loads and the connection between activity and demand. The stochastic nature of the demand is well-known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. This paper presents evidence that it is feasible to generate stochastic models of activity-related electricity demand based on monitored data. Two machine learning approaches are used to develop stochastic models of plug loads; an autoencoder (AE) and a Functional Data Analysis (FDA) model. Using data from two office buildings located in different countries, the transferability of models is explored by training the models on data from one building and using the trained models to predict demand for the other building. The results show that both models predict plug loads satisfactorily, with a good agreement with the mean demand and quantification of the uncertainty.\n
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\n \n\n \n \n \n \n \n A Tool for Generation of Stochastic Occupant-Based Internal Loads Using a Functional Data Analysis Approach to Re-Define `Activity'.\n \n \n \n\n\n \n Ward, R. M., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 14(3): 303–327. 2021.\n \n\n\n\n
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@article{ward_activity_2021,\n  title = {A Tool for Generation of Stochastic Occupant-Based Internal Loads Using a Functional Data Analysis Approach to Re-Define `Activity'},\n  author = {Ward, R. M. and Choudhary, R.},\n  year = {2021},\n  journal = {Journal of Building Performance Simulation},\n  volume = {14},\n  number = {3},\n  pages = {303--327},\n  publisher = {Taylor \\& Francis},\n  doi = {10.1080/19401493.2021.1919209}\n}\n\n
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\n \n\n \n \n \n \n \n Continuous Calibration of a Digital Twin: Comparison of Particle Filter and Bayesian Calibration Approaches.\n \n \n \n\n\n \n Ward, R., Choudhary, R., Gregory, A., Jans-Singh, M., & Girolami, M.\n\n\n \n\n\n\n Data-Centric Engineering, 2: e15. 2021.\n \n\n\n\n
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@article{ward_calibration_2021,\n  title = {Continuous Calibration of a Digital Twin: {{Comparison}} of Particle Filter and {{Bayesian}} Calibration Approaches},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Gregory, Alastair and {Jans-Singh}, Melanie and Girolami, Mark},\n  year = {2021},\n  journal = {Data-Centric Engineering},\n  volume = {2},\n  pages = {e15},\n  publisher = {Cambridge University Press},\n  doi = {10.1017/dce.2021.12}\n}\n\n
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\n \n\n \n \n \n \n \n \n Co-Simulating a Greenhouse in a Building to Quantify Co-Benefits of Different Coupled Configurations.\n \n \n \n \n\n\n \n Jans-Singh, M., Ward, R., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 14(3): 247–276. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Co-SimulatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2021.1908426,\n  title = {Co-Simulating a Greenhouse in a Building to Quantify Co-Benefits of Different Coupled Configurations},\n  author = {{Jans-Singh}, Melanie and Ward, Rebecca and Choudhary, Ruchi},\n  year = {2021},\n  journal = {Journal of Building Performance Simulation},\n  volume = {14},\n  number = {3},\n  eprint = {https://doi.org/10.1080/19401493.2021.1908426},\n  pages = {247--276},\n  publisher = {Taylor \\& Francis},\n  doi = {10.1080/19401493.2021.1908426},\n  url = {https://doi.org/10.1080/19401493.2021.1908426}\n}\n\n
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\n \n\n \n \n \n \n \n A Data-Centric Stochastic Model for Simulation of Occupant-Related Energy Demand in Buildings.\n \n \n \n\n\n \n Ward, R. M.\n\n\n \n\n\n\n Ph.D. Thesis, University of Cambridge, 2021.\n \n\n\n\n
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@phdthesis{ward_phdthesis_2021,\n  title = {A Data-Centric Stochastic Model for Simulation of Occupant-Related Energy Demand in Buildings},\n  author = {Ward, R. M.},\n  year = {2021},\n  doi = {10.17863/CAM.75927},\n  school = {University of Cambridge}\n}\n\n
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\n \n\n \n \n \n \n \n A Clustering Approach to Clean Cooking Transition Pathways for Low-Income Households in Bangalore.\n \n \n \n\n\n \n Neto-Bradley, A. P., Rangarajan, R., Choudhary, R., & Bazaz, A.\n\n\n \n\n\n\n Sustainable Cities and Society, 66: 102697. 2021.\n \n\n\n\n
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@article{neto2021clustering,\n  title = {A Clustering Approach to Clean Cooking Transition Pathways for Low-Income Households in {{Bangalore}}},\n  author = {{Neto-Bradley}, Andr{\\'e} Paul and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir},\n  year = {2021},\n  journal = {Sustainable Cities and Society},\n  volume = {66},\n  pages = {102697},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Research Data Supporting\" a Clustering Approach to Clean Cooking Transition Pathways for Low-Income Households in Bangalore\".\n \n \n \n\n\n \n Neto-Bradley, A., Choudhary, R., Bazaz, A., & Rangarajan, R.\n\n\n \n\n\n\n . 2021.\n \n\n\n\n
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@article{neto2021research,\n  title = {Research Data Supporting" a Clustering Approach to Clean Cooking Transition Pathways for Low-Income Households in Bangalore"},\n  author = {{Neto-Bradley}, Andre and Choudhary, Ruchi and Bazaz, Amir and Rangarajan, Rishika},\n  year = {2021}\n}\n\n
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\n \n\n \n \n \n \n \n Quantifying Resilience in Energy Systems with Out-of-Sample Testing.\n \n \n \n\n\n \n Pickering, B., & Choudhary, R.\n\n\n \n\n\n\n Applied Energy, 285: 116465. 2021.\n \n\n\n\n
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@article{pickering2021quantifying,\n  title = {Quantifying Resilience in Energy Systems with Out-of-Sample Testing},\n  author = {Pickering, Bryn and Choudhary, Ruchi},\n  year = {2021},\n  journal = {Applied Energy},\n  volume = {285},\n  pages = {116465},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Development of Chiller-Attached Apparatus for Accurate Initial Ground Temperature Measurement: Insights from Global Sensitivity Analysis of Thermal Response Tests.\n \n \n \n\n\n \n Choi, W., Choudhary, R., & Ooka, R.\n\n\n \n\n\n\n Energy and Buildings, 238: 110841. 2021.\n \n\n\n\n
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@article{choi2021development,\n  title = {Development of Chiller-Attached Apparatus for Accurate Initial Ground Temperature Measurement: {{Insights}} from Global Sensitivity Analysis of Thermal Response Tests},\n  author = {Choi, Wonjun and Choudhary, Ruchi and Ooka, Ryozo},\n  year = {2021},\n  journal = {Energy and Buildings},\n  volume = {238},\n  pages = {110841},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Impacts of Underground Climate Change on Urban Geothermal Potential: Lessons Learnt from a Case Study in London.\n \n \n \n\n\n \n Bidarmaghz, A., Choudhary, R., Narsilio, G., & Soga, K.\n\n\n \n\n\n\n Science of The Total Environment, 778: 146196. 2021.\n \n\n\n\n
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@article{bidarmaghz2021impacts,\n  title = {Impacts of Underground Climate Change on Urban Geothermal Potential: {{Lessons}} Learnt from a Case Study in {{London}}},\n  author = {Bidarmaghz, Asal and Choudhary, Ruchi and Narsilio, Guillermo and Soga, Kenichi},\n  year = {2021},\n  journal = {Science of The Total Environment},\n  volume = {778},\n  pages = {146196},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Impact of Simplifications on Numerical Modelling of the Shallow Subsurface at City-Scale and Implications for Shallow Geothermal Potential.\n \n \n \n\n\n \n Makasis, N., Kreitmair, e., Bidarmaghz, A, Farr, e., Scheidegger, e., & Choudhary, R.\n\n\n \n\n\n\n Science of The Total Environment, 791: 148236. 2021.\n \n\n\n\n
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@article{makasis2021impact,\n  title = {Impact of Simplifications on Numerical Modelling of the Shallow Subsurface at City-Scale and Implications for Shallow Geothermal Potential},\n  author = {Makasis, Nikolas and Kreitmair, {\\relax MJ} and Bidarmaghz, A and Farr, {\\relax GJ} and Scheidegger, {\\relax JM} and Choudhary, Ruchi},\n  year = {2021},\n  journal = {Science of The Total Environment},\n  volume = {791},\n  pages = {148236},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n In Memoriam Professor Godfried Augenbroe (1948–2021).\n \n \n \n\n\n \n de Wilde , P., Choudhary, R., & Park, C.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
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@misc{de2021memoriam,\n  title = {In Memoriam Professor Godfried Augenbroe (1948--2021)},\n  author = {{de Wilde}, Pieter and Choudhary, Ruchi and Park, Cheol-Soo},\n  year = {2021},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Professor Godfried (Fried) Augenbroe.\n \n \n \n\n\n \n Choudhary, R., Park, C. S., & De Wilde, P.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
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@misc{choudhary2021professor,\n  title = {Professor Godfried (Fried) Augenbroe},\n  author = {Choudhary, Ruchi and Park, Cheol Soo and De Wilde, Pieter},\n  year = {2021},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n \n\n \n \n \n \n \n Energy Transition Pathways amongst Low-Income Urban Households: A Mixed Method Clustering Approach.\n \n \n \n\n\n \n Neto-Bradley, A. P, Rangarajan, R., Choudhary, R., & Bazaz, A. B\n\n\n \n\n\n\n MethodsX, 8: 101491. 2021.\n \n\n\n\n
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@article{neto2021energy,\n  title = {Energy Transition Pathways amongst Low-Income Urban Households: {{A}} Mixed Method Clustering Approach},\n  author = {{Neto-Bradley}, Andr{\\'e} P and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir B},\n  year = {2021},\n  journal = {MethodsX},\n  volume = {8},\n  pages = {101491},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n A Microsimulation of Spatial Inequality in Energy Access: A Bayesian Multi-Level Modelling Approach for Urban India.\n \n \n \n\n\n \n Neto-Bradley, A. P., Choudhary, R., & Challenor, P.\n\n\n \n\n\n\n arXiv preprint arXiv:2109.08577. 2021.\n \n\n\n\n
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@article{neto2021microsimulation,\n  title = {A Microsimulation of Spatial Inequality in Energy Access: {{A Bayesian}} Multi-Level Modelling Approach for Urban {{India}}},\n  author = {{Neto-Bradley}, Andr{\\'e} Paul and Choudhary, Ruchi and Challenor, Peter},\n  year = {2021},\n  journal = {arXiv preprint arXiv:2109.08577},\n  eprint = {2109.08577},\n  archiveprefix = {arXiv}\n}\n\n
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\n \n\n \n \n \n \n \n Re-Defining 'activity' in Buildings to Reflect Stochastic Energy Demand: A Functional Data Analysis Approach.\n \n \n \n\n\n \n Ward, R., & Choudhary, R.\n\n\n \n\n\n\n In Building Simulation and Optimisation 2020, 2020. \n \n\n\n\n
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@inproceedings{ward_activity_2020,\n  title = {Re-Defining 'activity' in Buildings to Reflect Stochastic Energy Demand: A Functional Data Analysis Approach},\n  booktitle = {Building {{Simulation}} and {{Optimisation}} 2020},\n  author = {Ward, Rebecca and Choudhary, Ruchi},\n  year = {2020}\n}\n\n
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\n \n\n \n \n \n \n \n Large-Scale Urban Underground Hydro-Thermal Modelling–A Case Study of the Royal Borough of Kensington and Chelsea, London.\n \n \n \n\n\n \n Bidarmaghz, A., Choudhary, R., Soga, K., Terrington, R. L, Kessler, H., & Thorpe, S.\n\n\n \n\n\n\n Science of the Total Environment, 700: 134955. 2020.\n \n\n\n\n
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@article{bidarmaghz2020large,\n  title = {Large-Scale Urban Underground Hydro-Thermal Modelling--{{A}} Case Study of the Royal Borough of Kensington and Chelsea, London},\n  author = {Bidarmaghz, Asal and Choudhary, Ruchi and Soga, Kenichi and Terrington, Ricky L and Kessler, Holger and Thorpe, Stephen},\n  year = {2020},\n  journal = {Science of the Total Environment},\n  volume = {700},\n  pages = {134955},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Slipping through the Net: Can Data Science Approaches Help Target Clean Cooking Policy Interventions?.\n \n \n \n\n\n \n Neto-Bradley, A. P., Choudhary, R., & Bazaz, A.\n\n\n \n\n\n\n Energy Policy, 144: 111650. 2020.\n \n\n\n\n
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@article{neto2020slipping,\n  title = {Slipping through the Net: {{Can}} Data Science Approaches Help Target Clean Cooking Policy Interventions?},\n  author = {{Neto-Bradley}, Andr{\\'e} Paul and Choudhary, Ruchi and Bazaz, Amir},\n  year = {2020},\n  journal = {Energy Policy},\n  volume = {144},\n  pages = {111650},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Multi-Fidelity Approach to Bayesian Parameter Estimation in Subsurface Heat and Fluid Transport Models.\n \n \n \n\n\n \n Menberg, K., Bidarmaghz, A., Gregory, A., Choudhary, R., & Girolami, M.\n\n\n \n\n\n\n Science of The Total Environment, 745: 140846. 2020.\n \n\n\n\n
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@article{menberg2020multi,\n  title = {Multi-Fidelity Approach to {{Bayesian}} Parameter Estimation in Subsurface Heat and Fluid Transport Models},\n  author = {Menberg, Kathrin and Bidarmaghz, Asal and Gregory, Alastair and Choudhary, Ruchi and Girolami, Mark},\n  year = {2020},\n  journal = {Science of The Total Environment},\n  volume = {745},\n  pages = {140846},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n A Calibrated 3D Thermal Model of Urban Heat Fluxes into the Shallow Subsurface.\n \n \n \n\n\n \n Kreitmair, M., Bidarmaghz, A., Terrington, R., Farr, G., & Choudhary, R.\n\n\n \n\n\n\n In EGU General Assembly Conference Abstracts, pages 22207, 2020. \n \n\n\n\n
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@inproceedings{kreitmair2020calibrated,\n  title = {A Calibrated {{3D}} Thermal Model of Urban Heat Fluxes into the Shallow Subsurface},\n  booktitle = {{{EGU}} General Assembly Conference Abstracts},\n  author = {Kreitmair, Monika and Bidarmaghz, Asal and Terrington, Ricky and Farr, Gareth and Choudhary, Ruchi},\n  year = {2020},\n  pages = {22207}\n}\n\n
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\n \n\n \n \n \n \n \n A Two-Step Clustering Framework for Locally Tailored Design of Residential Heating Policies.\n \n \n \n\n\n \n Yuan, M., & Choudhary, R.\n\n\n \n\n\n\n Sustainable Cities and Society, 63: 102431. 2020.\n \n\n\n\n
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@article{yuan2020two,\n  title = {A Two-Step Clustering Framework for Locally Tailored Design of Residential Heating Policies},\n  author = {Yuan, Mingda and Choudhary, Ruchi},\n  year = {2020},\n  journal = {Sustainable Cities and Society},\n  volume = {63},\n  pages = {102431},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Effective and Scalable Modelling of Existing Non-Domestic Buildings with Radiator System under Uncertainty.\n \n \n \n\n\n \n Li, Q., Choudhary, R., Heo, Y., & Augenbroe, G.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 13(6): 740–759. 2020.\n \n\n\n\n
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@article{li2020effective,\n  title = {Effective and Scalable Modelling of Existing Non-Domestic Buildings with Radiator System under Uncertainty},\n  author = {Li, Qi and Choudhary, Ruchi and Heo, Yeonsook and Augenbroe, Godfried},\n  year = {2020},\n  journal = {Journal of Building Performance Simulation},\n  volume = {13},\n  number = {6},\n  pages = {740--759},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n \n\n \n \n \n \n \n Effect of Anthropogenic Heat Sources in the Shallow Subsurface at City-Scale.\n \n \n \n\n\n \n Kreitmair, M. J, Makasis, N., Bidarmaghz, A., Terrington, R. L, Farr, G. J, Scheidegger, J. M, & Choudhary, R.\n\n\n \n\n\n\n In E3S Web of Conferences, volume 205, pages 07002, 2020. EDP Sciences\n \n\n\n\n
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@inproceedings{kreitmair2020effect,\n  title = {Effect of Anthropogenic Heat Sources in the Shallow Subsurface at City-Scale},\n  booktitle = {{{E3S}} Web of Conferences},\n  author = {Kreitmair, Monika J and Makasis, Nikolas and Bidarmaghz, Asal and Terrington, Ricky L and Farr, Gareth J and Scheidegger, Johanna M and Choudhary, Ruchi},\n  year = {2020},\n  volume = {205},\n  pages = {07002},\n  publisher = {EDP Sciences}\n}\n\n
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\n \n\n \n \n \n \n \n Digital Twin of an Urban-Integrated Hydroponic Farm.\n \n \n \n\n\n \n Jans-Singh, M., Leeming, K., Choudhary, R., & Girolami, M.\n\n\n \n\n\n\n Data-Centric Engineering, 1. 2020.\n \n\n\n\n
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@article{jans2020digital,\n  title = {Digital Twin of an Urban-Integrated Hydroponic Farm},\n  author = {{Jans-Singh}, Melanie and Leeming, Kathryn and Choudhary, Ruchi and Girolami, Mark},\n  year = {2020},\n  journal = {Data-Centric Engineering},\n  volume = {1},\n  publisher = {Cambridge University Press}\n}\n\n
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\n \n\n \n \n \n \n \n District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles.\n \n \n \n\n\n \n Pickering, B., & Choudhary, R.\n\n\n \n\n\n\n Applied Energy, 236: 1138–1157. February 2019.\n \n\n\n\n
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@article{Pickering_District_2019,\n  title = {District Energy System Optimisation under Uncertain Demand: {{Handling}} Data-Driven Stochastic Profiles},\n  shorttitle = {District Energy System Optimisation under Uncertain Demand},\n  author = {Pickering, B. and Choudhary, R.},\n  year = {2019},\n  month = feb,\n  journal = {Applied Energy},\n  volume = {236},\n  pages = {1138--1157},\n  issn = {0306-2619},\n  doi = {10.1016/j.apenergy.2018.12.037},\n  abstract = {Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.},\n  keywords = {Data-driven demand,District energy systems,Mixed integer linear optimisation,Scenario optimisation,Scenario reduction}\n}\n\n
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\n Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.\n
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\n \n\n \n \n \n \n \n \n A Data-Centric Bottom-up Model for Generation of Stochastic Internal Load Profiles Based on Space-Use Type.\n \n \n \n \n\n\n \n Ward, R. M., Choudhary, R., Heo, Y., & Aston, J. A. D.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 12(5): 620–636. 2019.\n \n\n\n\n
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@article{doi:10.1080/19401493.2019.1583287,\n  title = {A Data-Centric Bottom-up Model for Generation of Stochastic Internal Load Profiles Based on Space-Use Type},\n  author = {Ward, R. M. and Choudhary, R. and Heo, Y. and Aston, J. A. D.},\n  year = {2019},\n  journal = {Journal of Building Performance Simulation},\n  volume = {12},\n  number = {5},\n  eprint = {https://doi.org/10.1080/19401493.2019.1583287},\n  pages = {620--636},\n  publisher = {Taylor \\&amp; Francis},\n  doi = {10.1080/19401493.2019.1583287},\n  url = {https://doi.org/10.1080/19401493.2019.1583287}\n}\n\n
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\n \n\n \n \n \n \n \n A Structured Stochastic Model for Specification of Occupant Related End-Use Energy Demands in Building Energy Simulation.\n \n \n \n\n\n \n Ward, R., Choudhary, R., & Aston, J.\n\n\n \n\n\n\n In Technical Symposium, Sheffield, UK, 2019. CIBSE\n \n\n\n\n
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@inproceedings{ward_cibsets_2019,\n  title = {A Structured Stochastic Model for Specification of Occupant Related End-Use Energy Demands in Building Energy Simulation},\n  booktitle = {Technical Symposium},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Aston, John},\n  year = {2019},\n  publisher = {CIBSE},\n  address = {Sheffield, UK}\n}\n\n
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\n \n\n \n \n \n \n \n Influence of Error Terms in Bayesian Calibration of Energy System Models.\n \n \n \n\n\n \n Menberg, K., Heo, Y., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 12(1): 82–96. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{menberg2019influence,\n  title = {Influence of Error Terms in {{Bayesian}} Calibration of Energy System Models},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},\n  year = {2019},\n  journal = {Journal of Building Performance Simulation},\n  volume = {12},\n  number = {1},\n  pages = {82--96},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n \n\n \n \n \n \n \n Predicting Intra-Day Load Profiles under Time-of-Use Tariffs Using Smart Meter Data.\n \n \n \n\n\n \n Kiguchi, Y, Heo, Y, Weeks, M, & Choudhary, R\n\n\n \n\n\n\n Energy, 173: 959–970. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{kiguchi2019predicting,\n  title = {Predicting Intra-Day Load Profiles under Time-of-Use Tariffs Using Smart Meter Data},\n  author = {Kiguchi, Y and Heo, Y and Weeks, M and Choudhary, R},\n  year = {2019},\n  journal = {Energy},\n  volume = {173},\n  pages = {959--970},\n  publisher = {Pergamon}\n}\n\n
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\n \n\n \n \n \n \n \n Linking the Urban-Scale Building Energy Demands with City Breathability and Urban Form Characteristics.\n \n \n \n\n\n \n Mouzourides, P., Kyprianou, A., Neophytou, M. K., Ching, J., & Choudhary, R.\n\n\n \n\n\n\n Sustainable cities and society, 49: 101460. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{mouzourides2019linking,\n  title = {Linking the Urban-Scale Building Energy Demands with City Breathability and Urban Form Characteristics},\n  author = {Mouzourides, Petros and Kyprianou, Andreas and Neophytou, Marina K-A and Ching, Jason and Choudhary, Ruchi},\n  year = {2019},\n  journal = {Sustainable cities and society},\n  volume = {49},\n  pages = {101460},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Making Geology Relevant for Infrastructure and Planning.\n \n \n \n\n\n \n Terrington, e., Thorpe, S, Kessler, H, Bidarmaghz, A, Choudhary, R, Yuan, M, & Bricker, S\n\n\n \n\n\n\n In International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data-Informed Decision-Making, pages 403–409, 2019. ICE Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{terrington2019making,\n  title = {Making Geology Relevant for Infrastructure and Planning},\n  booktitle = {International Conference on Smart Infrastructure and Construction 2019 ({{ICSIC}}) Driving Data-Informed Decision-Making},\n  author = {Terrington, {\\relax RL} and Thorpe, S and Kessler, H and Bidarmaghz, A and Choudhary, R and Yuan, M and Bricker, S},\n  year = {2019},\n  pages = {403--409},\n  publisher = {ICE Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Tailoring Residential Energy Provision Strategies in Fast-Growing Cities Using Targeted Data Collection.\n \n \n \n\n\n \n Neto-Bradley, e., Choudhary, R., & Bazaz, e.\n\n\n \n\n\n\n In International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data-Informed Decision-Making, pages 151–160, 2019. ICE Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{neto2019tailoring,\n  title = {Tailoring Residential Energy Provision Strategies in Fast-Growing Cities Using Targeted Data Collection},\n  booktitle = {International Conference on Smart Infrastructure and Construction 2019 ({{ICSIC}}) Driving Data-Informed Decision-Making},\n  author = {{Neto-Bradley}, {\\relax AP} and Choudhary, Ruchi and Bazaz, {\\relax AB}},\n  year = {2019},\n  pages = {151--160},\n  publisher = {ICE Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Monitoring the Performance of an Underground Hydroponic Farm.\n \n \n \n\n\n \n Jans-Singh, M, Fidler, P, Ward, e., & Choudhary, R\n\n\n \n\n\n\n In International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data-Informed Decision-Making, pages 133–141, 2019. ICE Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{jans2019monitoring,\n  title = {Monitoring the Performance of an Underground Hydroponic Farm},\n  booktitle = {International Conference on Smart Infrastructure and Construction 2019 ({{ICSIC}}) Driving Data-Informed Decision-Making},\n  author = {{Jans-Singh}, M and Fidler, P and Ward, {\\relax RM} and Choudhary, R},\n  year = {2019},\n  pages = {133--141},\n  publisher = {ICE Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Influence of Geology and Hydrogeology on Heat Rejection from Residential Basements in Urban Areas.\n \n \n \n\n\n \n Bidarmaghz, A., Choudhary, R., Soga, K., Kessler, H., Terrington, R. L, & Thorpe, S.\n\n\n \n\n\n\n Tunnelling and Underground Space Technology, 92: 103068. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{bidarmaghz2019influence,\n  title = {Influence of Geology and Hydrogeology on Heat Rejection from Residential Basements in Urban Areas},\n  author = {Bidarmaghz, Asal and Choudhary, Ruchi and Soga, Kenichi and Kessler, Holger and Terrington, Ricky L and Thorpe, Stephen},\n  year = {2019},\n  journal = {Tunnelling and Underground Space Technology},\n  volume = {92},\n  pages = {103068},\n  publisher = {Pergamon}\n}\n\n
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\n \n\n \n \n \n \n \n Applicability of an `Uptake Wave'Energy Transition Concept in Indian Households.\n \n \n \n\n\n \n Neto-Bradley, e., Choudhary, R., & Bazaz, e.\n\n\n \n\n\n\n In IOP Conference Series: Earth and Environmental Science, volume 294, pages 012091, 2019. IOP Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{neto2019applicability,\n  title = {Applicability of an `Uptake Wave'Energy Transition Concept in {{Indian}} Households},\n  booktitle = {{{IOP}} Conference Series: {{Earth}} and Environmental Science},\n  author = {{Neto-Bradley}, {\\relax AP} and Choudhary, Ruchi and Bazaz, {\\relax AB}},\n  year = {2019},\n  volume = {294},\n  pages = {012091},\n  publisher = {IOP Publishing}\n}\n\n
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\n  \n 2018\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n Calliope: A Multi-Scale Energy Systems Modelling Framework.\n \n \n \n\n\n \n Pfenninger, S., & Pickering, B.\n\n\n \n\n\n\n The Journal of Open Source Software, 3(29): 825. September 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Pfenninger_Calliope_2018,\n  title = {Calliope: A Multi-Scale Energy Systems Modelling Framework},\n  shorttitle = {Calliope},\n  author = {Pfenninger, Stefan and Pickering, Bryn},\n  year = {2018},\n  month = sep,\n  journal = {The Journal of Open Source Software},\n  volume = {3},\n  number = {29},\n  pages = {825},\n  doi = {10.21105/joss.00825},\n  langid = {english}\n}\n\n
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\n \n\n \n \n \n \n \n Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation.\n \n \n \n\n\n \n Pickering, B., & Choudhary, R.\n\n\n \n\n\n\n In Proceedings of BSO 2018, pages 38–45, Cambridge, UK, September 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Pickering_Mitigating_2018,\n  title = {Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation},\n  booktitle = {Proceedings of {{BSO}} 2018},\n  author = {Pickering, Bryn and Choudhary, Ruchi},\n  year = {2018},\n  month = sep,\n  pages = {38--45},\n  address = {Cambridge, UK},\n  abstract = {Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.},\n  langid = {english}\n}\n\n
\n
\n\n\n
\n Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.\n
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\n \n\n \n \n \n \n \n A Stochastic Data-Centric Model for Quantification of End-Use Energy Demand in Buildings.\n \n \n \n\n\n \n Ward, R., Choudhary, R., & Aston, J.\n\n\n \n\n\n\n In Building Simulation and Optimisation 2018, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ward_end-use_2018,\n  title = {A Stochastic Data-Centric Model for Quantification of End-Use Energy Demand in Buildings},\n  booktitle = {Building {{Simulation}} and {{Optimisation}} 2018},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Aston, John},\n  year = {2018}\n}\n\n
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\n \n\n \n \n \n \n \n Bayesian Inference for Thermal Response Test Parameter Estimation and Uncertainty Assessment.\n \n \n \n\n\n \n Choi, W., Kikumoto, H., Choudhary, R., & Ooka, R.\n\n\n \n\n\n\n Applied Energy, 209: 306–321. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{choi2018bayesian,\n  title = {Bayesian Inference for Thermal Response Test Parameter Estimation and Uncertainty Assessment},\n  author = {Choi, Wonjun and Kikumoto, Hideki and Choudhary, Ruchi and Ooka, Ryozo},\n  year = {2018},\n  journal = {Applied Energy},\n  volume = {209},\n  pages = {306--321},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Multi-Dimensional Simulation of Underground Subway Spaces Coupled with Geoenergy Systems.\n \n \n \n\n\n \n Mortada, A, Choudhary, R., & Soga, K\n\n\n \n\n\n\n Journal of Building Performance Simulation, 11(5): 517–537. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{mortada2018multi,\n  title = {Multi-Dimensional Simulation of Underground Subway Spaces Coupled with Geoenergy Systems},\n  author = {Mortada, A and Choudhary, Ruchi and Soga, K},\n  year = {2018},\n  journal = {Journal of Building Performance Simulation},\n  volume = {11},\n  number = {5},\n  pages = {517--537},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n \n\n \n \n \n \n \n Bayesian Inference of Structural Error in Inverse Models of Thermal Response Tests.\n \n \n \n\n\n \n Choi, W., Menberg, K., Kikumoto, H., Heo, Y., Choudhary, R., & Ooka, R.\n\n\n \n\n\n\n Applied Energy, 228: 1473–1485. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{choi2018bayesian,\n  title = {Bayesian Inference of Structural Error in Inverse Models of Thermal Response Tests},\n  author = {Choi, Wonjun and Menberg, Kathrin and Kikumoto, Hideki and Heo, Yeonsook and Choudhary, Ruchi and Ooka, Ryozo},\n  year = {2018},\n  journal = {Applied Energy},\n  volume = {228},\n  pages = {1473--1485},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Learning about Error Terms in Energy Models by Bayesian Calibration.\n \n \n \n\n\n \n Menberg, K., Heo, Y., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{menberg2018learning,\n  title = {Learning about Error Terms in Energy Models by Bayesian Calibration},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},\n  year = {2018},\n  journal = {Journal of Building Performance Simulation}\n}\n\n
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\n \n\n \n \n \n \n \n Quantifying the Environmental and Energy Benefits of Food Growth in the Urban Environment.\n \n \n \n\n\n \n Ward, R., Jans-Singh, M., & Choudhary, R.\n\n\n \n\n\n\n In Smart Plant Factory, pages 245–287. Springer, Singapore, 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@incollection{ward2018quantifying,\n  title = {Quantifying the Environmental and Energy Benefits of Food Growth in the Urban Environment},\n  booktitle = {Smart Plant Factory},\n  author = {Ward, Rebecca and {Jans-Singh}, Melanie and Choudhary, Ruchi},\n  year = {2018},\n  pages = {245--287},\n  publisher = {Springer, Singapore}\n}\n\n
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\n \n\n \n \n \n \n \n An Integrated Spatial Analysis Computer Environment for Urban-Building Energy in Cities.\n \n \n \n\n\n \n Sun, Y., Silva, E. A, Tian, W., Choudhary, R., & Leng, H.\n\n\n \n\n\n\n Sustainability, 10(11): 4235. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{sun2018integrated,\n  title = {An Integrated Spatial Analysis Computer Environment for Urban-Building Energy in Cities},\n  author = {Sun, Yu and Silva, Elisabete A and Tian, Wei and Choudhary, Ruchi and Leng, Hong},\n  year = {2018},\n  journal = {Sustainability},\n  volume = {10},\n  number = {11},\n  pages = {4235},\n  publisher = {Multidisciplinary Digital Publishing Institute}\n}\n\n
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\n  \n 2017\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n \n Applying Piecewise Linear Characteristic Curves in District Energy Optimisation.\n \n \n \n \n\n\n \n Pickering, B., & Choudhary, R.\n\n\n \n\n\n\n In The 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS), San Diego, California, USA, July 2017. \n \n\n\n\n
\n\n\n\n \n \n \"ApplyingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{Pickering2017ApplyingPiecewise,\n  title = {Applying Piecewise Linear Characteristic Curves in District Energy Optimisation},\n  booktitle = {The 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems ({{ECOS}})},\n  author = {Pickering, Bryn and Choudhary, Ruchi},\n  year = {2017},\n  month = jul,\n  address = {San Diego, California, USA},\n  url = {https://www.researchgate.net/publication/319334427_Applying_Piecewise_Linear_Characteristic_Curves_in_District_Energy_Optimisation},\n  abstract = {Representing nonlinear curves as piecewise elements allows complex systems to be optimised by linear programs. Piecewise linearisation has been recently introduced in the context of distributed energy system optimisation. It is an efficient technique for representing non-linear technology behaviours in linear optimisation models, which are favourable in district energy optimisation models, owing to their speed and ability to handle large numbers of design variables. This paper describes a method of automating the creation of piecewise elements of technology performance curves for minimum fit error. The results show an objective function value improvement at a relatively large penalty in solution time: from 1.6 times to 58 times longer than describing technologies as having a single value for efficiency (SVE). We show that within the context of common technology performance curves, three breakpoints yield sufficiently accurate results and any returns are diminishing beyond that. Even at three breakpoints, it is evident that the placement of breakpoints along a curve significantly influences solution time, in a way for which it is not possible to account in automation. But, large savings can be made by automation by including a constraint to ensure piecewise curves have a strictly increasing/decreasing gradient. This avoids the use of special ordered sets, simplifying model generation and the number of non-continuous variables. SVE models provide a less realistic solution and application of nonlinear consumption curves ex-post shows them to be ultimately more expensive systems than their piecewise counterparts. However, this ex-post analysis applied to SVE models is a good compromise for feasibility level analyses, where whole system cost is key. However, investment decisions and operation schedules are markedly affected by consumption curve representation. Thus, the use of piecewise linearisation is beneficial for detailed design, particularly if automation of breakpoint allocation can help solve the issue of model convergence.},\n  project = {District\\_energy},\n  keywords = {District energy,Mixed Integer Linear Programming,Optimisation,Piecewise Linearisation}\n}\n\n
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\n\n\n
\n Representing nonlinear curves as piecewise elements allows complex systems to be optimised by linear programs. Piecewise linearisation has been recently introduced in the context of distributed energy system optimisation. It is an efficient technique for representing non-linear technology behaviours in linear optimisation models, which are favourable in district energy optimisation models, owing to their speed and ability to handle large numbers of design variables. This paper describes a method of automating the creation of piecewise elements of technology performance curves for minimum fit error. The results show an objective function value improvement at a relatively large penalty in solution time: from 1.6 times to 58 times longer than describing technologies as having a single value for efficiency (SVE). We show that within the context of common technology performance curves, three breakpoints yield sufficiently accurate results and any returns are diminishing beyond that. Even at three breakpoints, it is evident that the placement of breakpoints along a curve significantly influences solution time, in a way for which it is not possible to account in automation. But, large savings can be made by automation by including a constraint to ensure piecewise curves have a strictly increasing/decreasing gradient. This avoids the use of special ordered sets, simplifying model generation and the number of non-continuous variables. SVE models provide a less realistic solution and application of nonlinear consumption curves ex-post shows them to be ultimately more expensive systems than their piecewise counterparts. However, this ex-post analysis applied to SVE models is a good compromise for feasibility level analyses, where whole system cost is key. However, investment decisions and operation schedules are markedly affected by consumption curve representation. Thus, the use of piecewise linearisation is beneficial for detailed design, particularly if automation of breakpoint allocation can help solve the issue of model convergence.\n
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\n \n\n \n \n \n \n \n A Functional Principal Components Model for Internal Loads in Building Energy Simulation.\n \n \n \n\n\n \n Ward, R. M., Choudhary, R., Heo, Y., & Aston, J. A.\n\n\n \n\n\n\n In Building Simulation 2017, August 2017. International Building Performance Simulation Association\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ward_functional_2017,\n  title = {A Functional Principal Components Model for Internal Loads in Building Energy Simulation},\n  booktitle = {Building {{Simulation}} 2017},\n  author = {Ward, Rebecca Mary and Choudhary, Ruchi and Heo, Yeonsook and Aston, John AD},\n  year = {2017},\n  month = aug,\n  publisher = {International Building Performance Simulation Association},\n  abstract = {There is currently no established methodology for quantifying uncertainty in occupant-related building internal loads. In this paper, we propose that distinct spaces within a building may be assigned an opera- tional signature comprising the daily base load, load range and diversity profile. A Functional Data Anal- ysis (FDA) approach has been used to analyse moni- tored electricity consumption data for the derivation of such signatures. This approach enables simula- tion of the inherent stochasticity. It represents a step forward towards an ability to propagate uncertainty through a building energy simulation and to quantify the change in electricity consumption associated with a change in building operation.}\n}\n\n
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\n There is currently no established methodology for quantifying uncertainty in occupant-related building internal loads. In this paper, we propose that distinct spaces within a building may be assigned an opera- tional signature comprising the daily base load, load range and diversity profile. A Functional Data Anal- ysis (FDA) approach has been used to analyse moni- tored electricity consumption data for the derivation of such signatures. This approach enables simula- tion of the inherent stochasticity. It represents a step forward towards an ability to propagate uncertainty through a building energy simulation and to quantify the change in electricity consumption associated with a change in building operation.\n
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\n \n\n \n \n \n \n \n How Can a Multi-Scale Analysis Guide Smart Urban Energy Demand Management? An Example from London City Westminster Borough.\n \n \n \n\n\n \n Mouzourides, P, Kyprianou, A., Choudhary, R, Ching, J, & Neophytou, M.\n\n\n \n\n\n\n Procedia engineering, 180: 433–442. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{mouzourides2017can,\n  title = {How Can a Multi-Scale Analysis Guide Smart Urban Energy Demand Management? {{An}} Example from {{London City Westminster Borough}}},\n  author = {Mouzourides, P and Kyprianou, Andreas and Choudhary, R and Ching, J and Neophytou, MK-A},\n  year = {2017},\n  journal = {Procedia engineering},\n  volume = {180},\n  pages = {433--442},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Exergy Analysis of a Hybrid Ground-Source Heat Pump System.\n \n \n \n\n\n \n Menberg, K., Heo, Y., Choi, W., Ooka, R., Choudhary, R., & Shukuya, M.\n\n\n \n\n\n\n Applied Energy, 204: 31–46. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{menberg2017exergy,\n  title = {Exergy Analysis of a Hybrid Ground-Source Heat Pump System},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choi, Wonjun and Ooka, Ryozo and Choudhary, Ruchi and Shukuya, Masanori},\n  year = {2017},\n  journal = {Applied Energy},\n  volume = {204},\n  pages = {31--46},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Bayesian Calibration of Building Energy Models: Comparison of Predictive Accuracy Using Metered Utility Data of Different Temporal Resolution.\n \n \n \n\n\n \n Kristensen, M. H., Choudhary, R., & Petersen, S.\n\n\n \n\n\n\n Energy Procedia, 122: 277–282. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{kristensen2017bayesian,\n  title = {Bayesian Calibration of Building Energy Models: {{Comparison}} of Predictive Accuracy Using Metered Utility Data of Different Temporal Resolution},\n  author = {Kristensen, Martin Heine and Choudhary, Ruchi and Petersen, Steffen},\n  year = {2017},\n  journal = {Energy Procedia},\n  volume = {122},\n  pages = {277--282},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Efficiency and Reliability of Bayesian Calibration of Energy Supply System Models.\n \n \n \n\n\n \n Menberg, K., Heo, Y., & Choudhary, R.\n\n\n \n\n\n\n In Proceedings of the 15th IBPSA Building Simulation Conference, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{menberg2017efficiency,\n  title = {Efficiency and Reliability of Bayesian Calibration of Energy Supply System Models},\n  booktitle = {Proceedings of the 15th {{IBPSA}} Building Simulation Conference},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},\n  year = {2017}\n}\n\n
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\n \n\n \n \n \n \n \n Exergy Analysis of a Ground-Source Heat Pump System.\n \n \n \n\n\n \n Menberg, K., Heo, Y., Choi, W., Ooka, R., Choudhary, R., & Shukuya, M.\n\n\n \n\n\n\n In 15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017, pages 1334–1343, 2017. International Building Performance Simulation Association\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{menberg2017exergy,\n  title = {Exergy Analysis of a Ground-Source Heat Pump System},\n  booktitle = {15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choi, Wonjun and Ooka, Ryozo and Choudhary, Ruchi and Shukuya, Masanori},\n  year = {2017},\n  pages = {1334--1343},\n  publisher = {International Building Performance Simulation Association}\n}\n\n
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\n \n\n \n \n \n \n \n Bayesian Calibration of Residential Building Clusters Using a Single Geometric Building Representation.\n \n \n \n\n\n \n Kristensen, e., Choudhary, R, Pedersen, e., & Petersen, S\n\n\n \n\n\n\n In Proceedings of Building Simulation, pages 1294–1303, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{kristensen2017bayesian,\n  title = {Bayesian Calibration of Residential Building Clusters Using a Single Geometric Building Representation},\n  booktitle = {Proceedings of Building Simulation},\n  author = {Kristensen, {\\relax MH} and Choudhary, R and Pedersen, {\\relax RH} and Petersen, S},\n  year = {2017},\n  pages = {1294--1303}\n}\n\n
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\n \n\n \n \n \n \n \n Occupancy Based Thermal Energy Modelling in the Urban Residential Sector.\n \n \n \n\n\n \n Tröndle, T., & Choudhary, R.\n\n\n \n\n\n\n WIT Transactions on Ecology and the Environment, 224: 31–44. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{trondle2017occupancy,\n  title = {Occupancy Based Thermal Energy Modelling in the Urban Residential Sector},\n  author = {Tr{\\"o}ndle, Tim and Choudhary, Ruchi},\n  year = {2017},\n  journal = {WIT Transactions on Ecology and the Environment},\n  volume = {224},\n  pages = {31--44},\n  publisher = {WIT Press}\n}\n\n
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\n  \n 2016\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n \n Exploring the Impact of Different Parameterisations of Occupant-Related Internal Loads in Building Energy Simulation.\n \n \n \n \n\n\n \n Ward, R. M., Choudhary, R., Heo, Y., & Rysanek, A.\n\n\n \n\n\n\n Energy and Buildings, 123: 92–105. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ExploringPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Ward201692,\n  title = {Exploring the Impact of Different Parameterisations of Occupant-Related Internal Loads in Building Energy Simulation},\n  author = {Ward, Rebecca Mary and Choudhary, Ruchi and Heo, Yeonsook and Rysanek, Adam},\n  year = {2016},\n  journal = {Energy and Buildings},\n  volume = {123},\n  pages = {92--105},\n  issn = {0378-7788},\n  doi = {http://dx.doi.org/10.1016/j.enbuild.2016.04.050},\n  url = {http://www.sciencedirect.com/science/article/pii/S037877881630305X},\n  abstract = {A building energy simulation relies on accurate parameterisation of occupant-related internal loads to simulate a realistic energy balance within a building. The internal loads are inextricably linked to occupant behaviour, both directly through the contribution of occupant heat output to thermal energy balance and indirectly via the interactions between occupants, appliances and building services. While occupancy itself is difficult to measure directly, most buildings possess a wealth of data in the form of monitored electricity consumption in varying degrees of resolution. These data, particularly plug loads, may be used to inform the model of occupant-related internal loads. Different approaches to parameterisation of plug loads have been investigated, with the purpose of exploring the conditions that might lead to preference of one approach over another. The models have been tested through a case study and simulation results have been compared against a range of response variables. Conclusions have been drawn as to the most important features of plug load parameterisation for a model to be used for forecasting future demand.},\n  project = {b-bem},\n  keywords = {Building energy simulation,Electricity consumption,Non-domestic buildings,Occupancy-related internal loads,Plug loads,Stochastic analysis,Uncertainty quantification}\n}\n\n
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\n\n\n
\n A building energy simulation relies on accurate parameterisation of occupant-related internal loads to simulate a realistic energy balance within a building. The internal loads are inextricably linked to occupant behaviour, both directly through the contribution of occupant heat output to thermal energy balance and indirectly via the interactions between occupants, appliances and building services. While occupancy itself is difficult to measure directly, most buildings possess a wealth of data in the form of monitored electricity consumption in varying degrees of resolution. These data, particularly plug loads, may be used to inform the model of occupant-related internal loads. Different approaches to parameterisation of plug loads have been investigated, with the purpose of exploring the conditions that might lead to preference of one approach over another. The models have been tested through a case study and simulation results have been compared against a range of response variables. Conclusions have been drawn as to the most important features of plug load parameterisation for a model to be used for forecasting future demand.\n
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\n \n\n \n \n \n \n \n \n Indoor Air Quality and Energy Management through Real-Time Sensing in Commercial Buildings.\n \n \n \n \n\n\n \n Kumar, P., Martin, C., Morawska, L., Norford, L., Choudhary, R., Bell, M., & Leach, M.\n\n\n \n\n\n\n Energy and Buildings, 111: 145–153. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"IndoorPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{kumar_indoor_2016,\n  title = {Indoor Air Quality and Energy Management through Real-Time Sensing in Commercial Buildings},\n  author = {Kumar, P. and Martin, C. and Morawska, L. and Norford, L. and Choudhary, R. and Bell, M. and Leach, M.},\n  year = {2016},\n  journal = {Energy and Buildings},\n  volume = {111},\n  pages = {145--153},\n  doi = {10.1016/j.enbuild.2015.11.037},\n  url = {http://www.sciencedirect.com/science/article/pii/S0378778815304023},\n  abstract = {Rapid growth in the global population requires expansion of building stock, which in turn calls for increased energy demand. This demand varies in time and also between different buildings, yet, conventional methods are only able to provide mean energy levels per zone and are unable to capture this inhomogeneity, which is important to conserve energy. An additional challenge is that some of the attempts to conserve energy, through for example lowering of ventilation rates, have been shown to exacerbate another problem, which is unacceptable indoor air quality (IAQ). The rise of sensing technology over the past decade has shown potential to address both these issues simultaneously by providing high-resolution tempo-spatial data to systematically analyse the energy demand and its consumption as well as the impacts of measures taken to control energy consumption on IAQ. However, challenges remain in the development of affordable services for data analysis, deployment of large-scale real-time Urban environment sensing network and responding through Building Energy Management Systems. This article presents the fundamental drivers behind the rise of sensing technology for the management of energy and IAQ in urban built environments, highlights major challenges for their large-scale deployment and identifies the research gaps that should be closed by future investigations.}\n}\n\n
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\n Rapid growth in the global population requires expansion of building stock, which in turn calls for increased energy demand. This demand varies in time and also between different buildings, yet, conventional methods are only able to provide mean energy levels per zone and are unable to capture this inhomogeneity, which is important to conserve energy. An additional challenge is that some of the attempts to conserve energy, through for example lowering of ventilation rates, have been shown to exacerbate another problem, which is unacceptable indoor air quality (IAQ). The rise of sensing technology over the past decade has shown potential to address both these issues simultaneously by providing high-resolution tempo-spatial data to systematically analyse the energy demand and its consumption as well as the impacts of measures taken to control energy consumption on IAQ. However, challenges remain in the development of affordable services for data analysis, deployment of large-scale real-time Urban environment sensing network and responding through Building Energy Management Systems. This article presents the fundamental drivers behind the rise of sensing technology for the management of energy and IAQ in urban built environments, highlights major challenges for their large-scale deployment and identifies the research gaps that should be closed by future investigations.\n
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\n \n\n \n \n \n \n \n \n Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule.\n \n \n \n \n\n\n \n Pickering, B., Ikeda, S., Choudhary, R., & Ooka, R.\n\n\n \n\n\n\n In CLIMA 2016 - Proceedings of the 12th REHVA World Congress: Volume 6, Aalborg, Denmark, May 2016. \n \n\n\n\n
\n\n\n\n \n \n \"ComparisonPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{Pickering2016Comparisonof,\n  title = {Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule},\n  booktitle = {{{CLIMA}} 2016 - Proceedings of the 12th {{REHVA}} World Congress: Volume 6},\n  author = {Pickering, Bryn and Ikeda, Shintaro and Choudhary, Ruchi and Ooka, Ryozo},\n  year = {2016},\n  month = may,\n  address = {Aalborg, Denmark},\n  url = {http://vbn.aau.dk/files/233775414/paper_529.pdf},\n  abstract = {Increasing complexity of building energy systems has led to a wide range of methods to minimise cost of meeting demand for all types of energy. Metaheuristics and mixed integer linear programmes (MILP) are the two most prevalent optimisation methods in the field, with relative advantages which have not previously been compared under common criteria. The principle objective of this paper is to scrutinise these two optimisation methods when applied to a problem of finding the optimal operational schedule of an energy system serving a hotel. 11 technologies are modelled by both methods, but all exhibit nonlinear characteristics which must be linearised for use in MILP. Comparison of the two models results in variation between objective function below 1},\n  project = {district\\_energy},\n  keywords = {Energy System Optimisation,Epsilon constrained differential evolution,Metaheuristics,Mixed Integer Linear Programming}\n}\n\n
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\n\n\n
\n Increasing complexity of building energy systems has led to a wide range of methods to minimise cost of meeting demand for all types of energy. Metaheuristics and mixed integer linear programmes (MILP) are the two most prevalent optimisation methods in the field, with relative advantages which have not previously been compared under common criteria. The principle objective of this paper is to scrutinise these two optimisation methods when applied to a problem of finding the optimal operational schedule of an energy system serving a hotel. 11 technologies are modelled by both methods, but all exhibit nonlinear characteristics which must be linearised for use in MILP. Comparison of the two models results in variation between objective function below 1\n
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\n \n\n \n \n \n \n \n \n Sensitivity Analysis Methods for Building Energy Models: Comparingcomputational Costs and Extractable Information.\n \n \n \n \n\n\n \n Menberg, K., Heo, Y., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 133: 433–445. October 2016.\n \n\n\n\n
\n\n\n\n \n \n \"SensitivityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{Menberg2016Sensitivityanalysis,\n  title = {Sensitivity Analysis Methods for Building Energy Models: {{Comparingcomputational}} Costs and Extractable Information},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},\n  year = {2016},\n  month = oct,\n  journal = {Energy and Buildings},\n  volume = {133},\n  pages = {433--445},\n  doi = {10.1016/j.enbuild.2016.10.005},\n  url = {http://dx.doi.org/10.1016/j.enbuild.2016.10.005},\n  abstract = {Though sensitivity analysis has been widely applied in the context of building energy models (BEMs),there are few studies that investigate the performance of different sensitivity analysis methods in rela-tion to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models.We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morrismethod for parameter screening, (b) linear regression analysis (medium computational costs) and (c)Sobol method (high computational costs). It is revealed that the results from Morris method taking thecommonly used measure for parameter influence can be unstable, while using the median value yieldsrobust results for evaluations with small sample sizes. For the dominant parameters the results from allthree sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameterranking or the differentiation of influential and negligible parameters, the computationally costly quan-titative methods provide the same information for the model in this study as the computational efficientMorris method using the median value. Exploring different methods to investigate higher-order effectsand parameter interactions, reveals that correlation of elementary effects and parameter values in Morrismethod can also provide basic information about parameter interactions.},\n  project = {b-bem}\n}\n\n
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\n Though sensitivity analysis has been widely applied in the context of building energy models (BEMs),there are few studies that investigate the performance of different sensitivity analysis methods in rela-tion to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models.We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morrismethod for parameter screening, (b) linear regression analysis (medium computational costs) and (c)Sobol method (high computational costs). It is revealed that the results from Morris method taking thecommonly used measure for parameter influence can be unstable, while using the median value yieldsrobust results for evaluations with small sample sizes. For the dominant parameters the results from allthree sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameterranking or the differentiation of influential and negligible parameters, the computationally costly quan-titative methods provide the same information for the model in this study as the computational efficientMorris method using the median value. Exploring different methods to investigate higher-order effectsand parameter interactions, reveals that correlation of elementary effects and parameter values in Morrismethod can also provide basic information about parameter interactions.\n
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\n \n\n \n \n \n \n \n New Extension of Morris Method for Sensitivity Analysis of Building Energy Models.\n \n \n \n\n\n \n Menberg, K., Heo, Y., Augenbroe, G., & Choudhary, R.\n\n\n \n\n\n\n In Building Simulation and Optimization, Newcastle, UK, September 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Menberg2016Newextension,\n  title = {New Extension of {{Morris}} Method for Sensitivity Analysis of Building Energy Models},\n  booktitle = {Building Simulation and Optimization},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Augenbroe, Godfried and Choudhary, Ruchi},\n  year = {2016},\n  month = sep,\n  address = {Newcastle, UK},\n  abstract = {Sensitivity analysis is commonly used in numerical modelling to identify those inputs that have a large impact on model outcomes. We scrutinise the Morris method, known to be computationally efficient for parameter screening, through a case study. This paper demonstrates that the current Morris method with the absolute mean as measure of parameter ranking yields unstable results. We show that using the median value, which is less sensitive to outliers, yields more robust parameter rankings for evaluations with small sample sizes. The performance of the improved Morris method is validated against the variance-based sensitivity analysis. We also investigate correlations between elementary effects and parameter values and find that they can be efficiently used to identify higher-order parameter interactions from a single set of samples used in the Morris method.},\n  project = {b-bem}\n}\n\n
\n
\n\n\n
\n Sensitivity analysis is commonly used in numerical modelling to identify those inputs that have a large impact on model outcomes. We scrutinise the Morris method, known to be computationally efficient for parameter screening, through a case study. This paper demonstrates that the current Morris method with the absolute mean as measure of parameter ranking yields unstable results. We show that using the median value, which is less sensitive to outliers, yields more robust parameter rankings for evaluations with small sample sizes. The performance of the improved Morris method is validated against the variance-based sensitivity analysis. We also investigate correlations between elementary effects and parameter values and find that they can be efficiently used to identify higher-order parameter interactions from a single set of samples used in the Morris method.\n
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\n \n\n \n \n \n \n \n Data-Driven Bottom-up Approach for Modelling Internal Loads in Building Energy Simulation Using Functional Principal Components.\n \n \n \n\n\n \n Ward, R., Choudhary, R., Heo, Y., & Guillas, S.\n\n\n \n\n\n\n In Building Simulation and Optimisation 2016, Newcastle, September 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Ward2016Data-drivenbottom-up,\n  title = {Data-Driven Bottom-up Approach for Modelling Internal Loads in Building Energy Simulation Using Functional Principal Components},\n  booktitle = {Building Simulation and Optimisation 2016},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Heo, Yeonsook and Guillas, Serge},\n  year = {2016},\n  month = sep,\n  address = {Newcastle},\n  abstract = {Internal loads in a building are difficult to quantify efficiently in a way which envelopes existing demand yet permits estimation of the impact of changes in building operation. The standard characterisation by energy-use intensity and diversity profile is well established; while quantification of energy-use intensity is achievable using monitored data, there is no standard approach for quantification of diversity profiles. This paper investigates an efficient method for the representation of the shape of the diversity profile using a functional data analysis approach together with electricity consumption data monitored at a spatial resolution that permits correlation of consumption with space use type. The approach has been applied to a case study building and has been shown to give a good agreement with monitored electricity consumption data.},\n  project = {b-bem}\n}\n\n
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\n Internal loads in a building are difficult to quantify efficiently in a way which envelopes existing demand yet permits estimation of the impact of changes in building operation. The standard characterisation by energy-use intensity and diversity profile is well established; while quantification of energy-use intensity is achievable using monitored data, there is no standard approach for quantification of diversity profiles. This paper investigates an efficient method for the representation of the shape of the diversity profile using a functional data analysis approach together with electricity consumption data monitored at a spatial resolution that permits correlation of consumption with space use type. The approach has been applied to a case study building and has been shown to give a good agreement with monitored electricity consumption data.\n
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\n \n\n \n \n \n \n \n Efficient Energy Modelling of Heterogeneous Building Portfolios.\n \n \n \n\n\n \n Pacheco-Torres, R., Heo, Y., & Choudhary, R.\n\n\n \n\n\n\n Sustainable cities and society, 27: 49–64. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{pacheco2016efficient,\n  title = {Efficient Energy Modelling of Heterogeneous Building Portfolios},\n  author = {{Pacheco-Torres}, Rosal{\\'{\\i}}a and Heo, Yeonsook and Choudhary, Ruchi},\n  year = {2016},\n  journal = {Sustainable cities and society},\n  volume = {27},\n  pages = {49--64},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n New Extension of Morris Method for Sensitivity Analysis of Building Energy Models.\n \n \n \n\n\n \n Menberg, K., Heo, Y., Augenbroe, G., & Choudhary, R.\n\n\n \n\n\n\n Building Simulation & Optimization. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{menberg2016new,\n  title = {New Extension of {{Morris}} Method for Sensitivity Analysis of Building Energy Models},\n  author = {Menberg, Kathrin and Heo, Yeonsook and Augenbroe, Godfried and Choudhary, Ruchi},\n  year = {2016},\n  journal = {Building Simulation \\& Optimization}\n}\n\n
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\n \n\n \n \n \n \n \n Potential of District-Scale Geothermal Energy in Urban Cities.\n \n \n \n\n\n \n Soga, K, Zhang, Y, & Choudhary, R\n\n\n \n\n\n\n In Proceedings of the 1st International Conference on Energy Geotechnics, Kiel, Germany, pages 29–31, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{soga2016potential,\n  title = {Potential of District-Scale Geothermal Energy in Urban Cities},\n  booktitle = {Proceedings of the 1st International Conference on Energy Geotechnics, Kiel, Germany},\n  author = {Soga, K and Zhang, Y and Choudhary, R},\n  year = {2016},\n  pages = {29--31}\n}\n\n
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\n  \n 2015\n \n \n (18)\n \n \n
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\n \n\n \n \n \n \n \n \n Solar Energy and Urban Morphology: Scenarios for Increasing the Renewable Energy Potential of Neighbourhoods in London.\n \n \n \n \n\n\n \n Sarralde, J. J., Quinn, D. J., Wiesmann, D., & Steemers, K.\n\n\n \n\n\n\n Renewable Energy, 73: 10–17. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"SolarPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Sarralde201510,\n  title = {Solar Energy and Urban Morphology: {{Scenarios}} for Increasing the Renewable Energy Potential of Neighbourhoods in {{London}}},\n  author = {Sarralde, Juan Jos{\\~A}{\\copyright} and Quinn, David James and Wiesmann, Daniel and Steemers, Koen},\n  year = {2015},\n  journal = {Renewable Energy},\n  volume = {73},\n  pages = {10--17},\n  issn = {0960-1481},\n  doi = {http://dx.doi.org/10.1016/j.renene.2014.06.028},\n  url = {http://www.sciencedirect.com/science/article/pii/S0960148114003681},\n  abstract = {Abstract Amongst academics and practitioners working in the fields of urban planning and design, there has been an on-going discussion regarding the relationships between urban morphology and environmental sustainability. A main focus of analysis has been to investigate whether the form of cities and neighbourhoods can be related to their energy efficiency, especially regarding the energy intensity of buildings and transportation. However, to analyse the overall energy performance of urban systems, both the consumption and the generation of resources need to be assessed. In terms of urban environmental sustainability, the potential to generate renewable energy within the city boundaries is a research topic of growing interest, being solar energy one of the main resources available. This study uses neighbourhood-scale statistical models to explore the relationships between aggregated urban form descriptors and the potential to harvest solar energy within the city. Different possible scenarios of urban morphology in Greater London are analysed and variables of urban form are tested with the aim of increasing the solar energy potential of neighbourhoods. Results show that by optimising combinations of up to eight variables of urban form the solar irradiation of roofs could be increased by ca. 9},\n  keywords = {London,Neighbourhood,Renewable energy,Solar potential,Urban morphology}\n}\n\n
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\n\n\n
\n Abstract Amongst academics and practitioners working in the fields of urban planning and design, there has been an on-going discussion regarding the relationships between urban morphology and environmental sustainability. A main focus of analysis has been to investigate whether the form of cities and neighbourhoods can be related to their energy efficiency, especially regarding the energy intensity of buildings and transportation. However, to analyse the overall energy performance of urban systems, both the consumption and the generation of resources need to be assessed. In terms of urban environmental sustainability, the potential to generate renewable energy within the city boundaries is a research topic of growing interest, being solar energy one of the main resources available. This study uses neighbourhood-scale statistical models to explore the relationships between aggregated urban form descriptors and the potential to harvest solar energy within the city. Different possible scenarios of urban morphology in Greater London are analysed and variables of urban form are tested with the aim of increasing the solar energy potential of neighbourhoods. Results show that by optimising combinations of up to eight variables of urban form the solar irradiation of roofs could be increased by ca. 9\n
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\n \n\n \n \n \n \n \n Forecasting Passenger Fleet Fuel Consumption–a New Methodology to Include Uncertainty Analysis.\n \n \n \n\n\n \n Martin, N. P., Bishop, J. D., Choudhary, R., & Boies, A. M\n\n\n \n\n\n\n In Transportation Research Board 94th Annual Meeting, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{martin2015forecasting,\n  title = {Forecasting Passenger Fleet Fuel Consumption--a New Methodology to Include Uncertainty Analysis},\n  booktitle = {Transportation Research Board 94th Annual Meeting},\n  author = {Martin, Niall PD and Bishop, Justin DK and Choudhary, Ruchi and Boies, Adam M},\n  year = {2015},\n  number = {15-4186},\n  abstract = {The UK's light duty vehicle fleet is the largest end user of refined petroleum, accounting for 12.5}\n}\n\n
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\n The UK's light duty vehicle fleet is the largest end user of refined petroleum, accounting for 12.5\n
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\n \n\n \n \n \n \n \n \n Can \\p̌hantom\\UKp̌hantom\\\\ Passenger Vehicles Be Designed to Meet 2020 Emissions Targets? A Novel Methodology to Forecast Fuel Consumption with Uncertainty Analysis.\n \n \n \n \n\n\n \n Martin, N. P., Bishop, J. D., Choudhary, R., & Boies, A. M.\n\n\n \n\n\n\n Applied Energy, 157: 929–939. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"CanPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Martin2015929,\n  title = {Can \\{\\vphantom\\}{{UK}}\\vphantom\\{\\} Passenger Vehicles Be Designed to Meet 2020 Emissions Targets? {{A}} Novel Methodology to Forecast Fuel Consumption with Uncertainty Analysis},\n  author = {Martin, Niall P.D. and Bishop, Justin D.K. and Choudhary, Ruchi and Boies, Adam M.},\n  year = {2015},\n  journal = {Applied Energy},\n  volume = {157},\n  pages = {929--939},\n  issn = {0306-2619},\n  doi = {http://dx.doi.org/10.1016/j.apenergy.2015.03.044},\n  url = {http://www.sciencedirect.com/science/article/pii/S0306261915003281},\n  abstract = {Abstract Vehicle manufacturers are required to reduce their European sales-weighted emissions to 95 g CO2/km by 2020, with the aim of reducing on-road fleet fuel consumption. Nevertheless, current fuel consumption models are not suited for the European market and are unable to account for uncertainties when used to forecast passenger vehicle energy-use. Therefore, a new methodology is detailed herein to quantify new car fleet fuel consumption based on vehicle design metrics. The New European Driving Cycle (NEDC) is shown to underestimate on-road fuel consumption in Spark (SI) and Compression Ignition (CI) vehicles by an average of 16},\n  keywords = {Bayesian,Energy use,Fuel consumption,NEDC,Uncertainty analysis,Vehicle emissions targets}\n}\n\n
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\n Abstract Vehicle manufacturers are required to reduce their European sales-weighted emissions to 95 g CO2/km by 2020, with the aim of reducing on-road fleet fuel consumption. Nevertheless, current fuel consumption models are not suited for the European market and are unable to account for uncertainties when used to forecast passenger vehicle energy-use. Therefore, a new methodology is detailed herein to quantify new car fleet fuel consumption based on vehicle design metrics. The New European Driving Cycle (NEDC) is shown to underestimate on-road fuel consumption in Spark (SI) and Compression Ignition (CI) vehicles by an average of 16\n
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\n \n\n \n \n \n \n \n \n Household Electricity Use, Electric Vehicle Home-Charging and Distributed Photovoltaic Power Production in the City of Westminster.\n \n \n \n \n\n\n \n Munkhammar, J., Bishop, J. D., Sarralde, J. J., Tian, W., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 86: 439–448. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"HouseholdPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Munkhammar2015439,\n  title = {Household Electricity Use, Electric Vehicle Home-Charging and Distributed Photovoltaic Power Production in the City of {{Westminster}}},\n  author = {Munkhammar, Joakim and Bishop, Justin D.K. and Sarralde, Juan Jose and Tian, Wei and Choudhary, Ruchi},\n  year = {2015},\n  journal = {Energy and Buildings},\n  volume = {86},\n  pages = {439--448},\n  issn = {0378-7788},\n  doi = {http://dx.doi.org/10.1016/j.enbuild.2014.10.006},\n  url = {http://www.sciencedirect.com/science/article/pii/S0378778814008263},\n  abstract = {Abstract In this paper we investigate household electricity use, electric vehicle (EV) home-charging and distributed photovoltaic (PV) power production in a case study for the city of Westminster, London. Since it is economically beneficial to maximize \\{PV\\} power self-consumption in the \\{UK\\} context the power consumption/production patterns with/without introducing \\{EV\\} home-charging on the household level is investigated. Additionally, since this might have an effect on the electricity use on an aggregate of households a large-scale introduction of \\{EV\\} charging and \\{PV\\} power production in the entire city of Westminster is also investigated. Household electricity consumption and \\{EV\\} home-charging are modeled with a Markov-chain model. \\{PV\\} power production is estimated from solar irradiation data from Meteonorm for the location of Westminster combined with a model for photovoltaic power production on tilted planes. The available rooftop area is estimated from the \\{UK\\} map geographic information database. \\{EV\\} home-charging increases the household electricity use mainly during evening with a maximum during winter whereas \\{PV\\} produces power during daytime with maximum during summer. On the household level this mismatch introduces variability in power consumption/production, which is shown to be less prominent for the large-scale scenario of the entire city of Westminster.},\n  keywords = {Distributed photovoltaic power production,Electric vehicle home-charging,Household electricity use,Self-consumption}\n}\n\n
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\n Abstract In this paper we investigate household electricity use, electric vehicle (EV) home-charging and distributed photovoltaic (PV) power production in a case study for the city of Westminster, London. Since it is economically beneficial to maximize \\PV\\ power self-consumption in the \\UK\\ context the power consumption/production patterns with/without introducing \\EV\\ home-charging on the household level is investigated. Additionally, since this might have an effect on the electricity use on an aggregate of households a large-scale introduction of \\EV\\ charging and \\PV\\ power production in the entire city of Westminster is also investigated. Household electricity consumption and \\EV\\ home-charging are modeled with a Markov-chain model. \\PV\\ power production is estimated from solar irradiation data from Meteonorm for the location of Westminster combined with a model for photovoltaic power production on tilted planes. The available rooftop area is estimated from the \\UK\\ map geographic information database. \\EV\\ home-charging increases the household electricity use mainly during evening with a maximum during winter whereas \\PV\\ produces power during daytime with maximum during summer. On the household level this mismatch introduces variability in power consumption/production, which is shown to be less prominent for the large-scale scenario of the entire city of Westminster.\n
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\n \n\n \n \n \n \n \n \n Influence of \\p̌hantom\\GSHPp̌hantom\\\\ System Design Parameters on the Geothermal Application Capacity and Electricity Consumption at City-Scale for Westminster, London.\n \n \n \n \n\n\n \n Zhang, Y., Choudhary, R., & Soga, K.\n\n\n \n\n\n\n Energy and Buildings, 106: 3–12. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"InfluencePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{Zhang20153,\n  title = {Influence of \\{\\vphantom\\}{{GSHP}}\\vphantom\\{\\} System Design Parameters on the Geothermal Application Capacity and Electricity Consumption at City-Scale for {{Westminster}}, {{London}}},\n  author = {Zhang, Yi and Choudhary, R. and Soga, K.},\n  year = {2015},\n  journal = {Energy and Buildings},\n  volume = {106},\n  pages = {3--12},\n  issn = {0378-7788},\n  doi = {http://dx.doi.org/10.1016/j.enbuild.2015.07.065},\n  url = {http://www.sciencedirect.com/science/article/pii/S037877881530181X},\n  abstract = {Abstract A city-scale renewable energy network for heating and cooling can significantly contribute to reduction of fossil fuel utilization and meeting the renewable energy targets. Ground source heat pump (GSHP) system is a technology that transfers heat stored over long periods to/from the ground to heat/cool the buildings. In particular, a vertical closed loop \\{GSHP\\} is a viable choice in densely populated urban areas. In this study, an ArcGIS-based simulation model has been developed to examine how many vertical closed loop \\{GSHPs\\} can be feasibly installed at city scale without overusing the geothermal energy underground. City of Westminster, in London, is used as a case study to identify and map areas where \\{GSHPs\\} can serve as a viable option for heating and/or cooling. A parametric study has been conducted to investigate the influence of how space heating and cooling demand is quantified on the potential utility of \\{GSHP\\} systems. The influence of \\{COP\\} variation during operation is also examined. The operational variation of \\{COP\\} influences the electricity consumption of the \\{GSHP\\} systems. Therefore, a comprehensive analysis including the capital cost, C/D ratio distribution, energy demand, and financial risk is highly recommended for district-level planning of \\{GSHP\\} systems.},\n  keywords = {Building load estimation,City scale,COP,Electricity consumption,GSHP,Ratio of capacity to demand}\n}\n\n
\n
\n\n\n
\n Abstract A city-scale renewable energy network for heating and cooling can significantly contribute to reduction of fossil fuel utilization and meeting the renewable energy targets. Ground source heat pump (GSHP) system is a technology that transfers heat stored over long periods to/from the ground to heat/cool the buildings. In particular, a vertical closed loop \\GSHP\\ is a viable choice in densely populated urban areas. In this study, an ArcGIS-based simulation model has been developed to examine how many vertical closed loop \\GSHPs\\ can be feasibly installed at city scale without overusing the geothermal energy underground. City of Westminster, in London, is used as a case study to identify and map areas where \\GSHPs\\ can serve as a viable option for heating and/or cooling. A parametric study has been conducted to investigate the influence of how space heating and cooling demand is quantified on the potential utility of \\GSHP\\ systems. The influence of \\COP\\ variation during operation is also examined. The operational variation of \\COP\\ influences the electricity consumption of the \\GSHP\\ systems. Therefore, a comprehensive analysis including the capital cost, C/D ratio distribution, energy demand, and financial risk is highly recommended for district-level planning of \\GSHP\\ systems.\n
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\n \n\n \n \n \n \n \n \n Economic, Climate Change, and Air Quality Analysis of Distributed Energy Resource Systems.\n \n \n \n \n\n\n \n Omu, A., Rysanek, A., Stettler, M., & Choudhary, R.\n\n\n \n\n\n\n Procedia Computer Science, 51: 2147–2156. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Economic,Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Omu20152147,\n  title = {Economic, Climate Change, and Air Quality Analysis of Distributed Energy Resource Systems},\n  author = {Omu, Akomeno and Rysanek, Adam and Stettler, Marc and Choudhary, Ruchi},\n  year = {2015},\n  journal = {Procedia Computer Science},\n  volume = {51},\n  pages = {2147--2156},\n  issn = {1877-0509},\n  doi = {http://dx.doi.org/10.1016/j.procs.2015.05.487},\n  url = {http://www.sciencedirect.com/science/article/pii/S1877050915012958},\n  abstract = {Abstract This paper presents an optimisation model and cost-benefit analysis framework for the quantification of the economic, climate change, and air quality impacts of the installation of a distributed energy resource system in the area surrounding Paddington train station in London, England. A mixed integer linear programming model, called the Distributed Energy Network Optimisation (DENO) model, is employed to design the optimal energy system for the district. \\{DENO\\} is then integrated into a cost-benefit analysis framework that determines the resulting monetised climate change and air quality impacts of the optimal energy systems for different technology scenarios in order to determine their overall economic and environmental impacts.},\n  keywords = {Air Quality,Distributed Energy Resource Systems,MILP,Optimisation}\n}\n\n
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\n Abstract This paper presents an optimisation model and cost-benefit analysis framework for the quantification of the economic, climate change, and air quality impacts of the installation of a distributed energy resource system in the area surrounding Paddington train station in London, England. A mixed integer linear programming model, called the Distributed Energy Network Optimisation (DENO) model, is employed to design the optimal energy system for the district. \\DENO\\ is then integrated into a cost-benefit analysis framework that determines the resulting monetised climate change and air quality impacts of the optimal energy systems for different technology scenarios in order to determine their overall economic and environmental impacts.\n
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\n \n\n \n \n \n \n \n \n Evaluation of Calibration Efficacy under Different Levels of Uncertainty.\n \n \n \n \n\n\n \n Heo, Y., Graziano, D. J., Guzowski, L., & Muehleisen, R. T.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 8(3): 135–144. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"EvaluationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2014.896947,\n  title = {Evaluation of Calibration Efficacy under Different Levels of Uncertainty},\n  author = {Heo, Yeonsook and Graziano, Diane J. and Guzowski, Leah and Muehleisen, Ralph T.},\n  year = {2015},\n  journal = {Journal of Building Performance Simulation},\n  volume = {8},\n  number = {3},\n  eprint = {http://dx.doi.org/10.1080/19401493.2014.896947},\n  pages = {135--144},\n  doi = {10.1080/19401493.2014.896947},\n  url = {http://dx.doi.org/10.1080/19401493.2014.896947},\n  abstract = {This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.}\n}\n\n
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\n This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.\n
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\n \n\n \n \n \n \n \n \n DELORES - an Open-Source Tool for Stochastic Prediction of Occupant Services Demand.\n \n \n \n \n\n\n \n Rysanek, A. M., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 8(2): 97–118. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"DELORESPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2014.888595,\n  title = {{{DELORES}} - an Open-Source Tool for Stochastic Prediction of Occupant Services Demand},\n  author = {Rysanek, Adam Martin and Choudhary, Ruchi},\n  year = {2015},\n  journal = {Journal of Building Performance Simulation},\n  volume = {8},\n  number = {2},\n  eprint = {http://dx.doi.org/10.1080/19401493.2014.888595},\n  pages = {97--118},\n  doi = {10.1080/19401493.2014.888595},\n  url = {http://dx.doi.org/10.1080/19401493.2014.888595},\n  abstract = {This paper provides a detailed technical description of DEmand LOad REconStructor (DELORES), a new, open-source modelling tool for stochastic simulation of occupant services demand in buildings. By services, one means usually: illumination, sustenance (by way of food preparation), communication (by way of information technology services), and thermal comfort. In the building simulation environment, however, these services are commonly represented through annual transient profiles of internal heat gains, electricity loads, and set point temperatures. The intended capability of DELORES is to stochastically generate such profiles for use in common building energy models, such as TRNSYS and EnergyPlus, and do so whilst presenting users with a straight-forward, easy-to-use interface. By furthering the dissemination of this tool in the public domain, and encouraging it is future development, it is hoped that multidisciplinary research related to stochastic occupant behaviour and energy demand will become an easier task.},\n  project = {delores}\n}\n\n
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\n This paper provides a detailed technical description of DEmand LOad REconStructor (DELORES), a new, open-source modelling tool for stochastic simulation of occupant services demand in buildings. By services, one means usually: illumination, sustenance (by way of food preparation), communication (by way of information technology services), and thermal comfort. In the building simulation environment, however, these services are commonly represented through annual transient profiles of internal heat gains, electricity loads, and set point temperatures. The intended capability of DELORES is to stochastically generate such profiles for use in common building energy models, such as TRNSYS and EnergyPlus, and do so whilst presenting users with a straight-forward, easy-to-use interface. By furthering the dissemination of this tool in the public domain, and encouraging it is future development, it is hoped that multidisciplinary research related to stochastic occupant behaviour and energy demand will become an easier task.\n
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\n \n\n \n \n \n \n \n \n Multi-Dimensional Simulation of Underground Spaces Coupled with Geoenergy Systems.\n \n \n \n \n\n\n \n Mortada, A., Choudhary, R., & Soga, K.\n\n\n \n\n\n\n In 14th International Conference of IBPSA, Building Simulation 2015, pages 2301–2308, Hyderabad, 2015. IBPSA\n \n\n\n\n
\n\n\n\n \n \n \"Multi-DimensionalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{mortada_multi-dimensional_2015,\n  title = {Multi-Dimensional Simulation of Underground Spaces Coupled with Geoenergy Systems},\n  booktitle = {14th {{International Conference}} of {{IBPSA}}, {{Building Simulation}} 2015},\n  author = {Mortada, A. and Choudhary, R. and Soga, K.},\n  year = {2015},\n  pages = {2301--2308},\n  publisher = {IBPSA},\n  address = {Hyderabad},\n  url = {http://www.bs2015.in/files/BS2015_Proceeding.pdf},\n  abstract = {Old and deep subway lines suffer from overheating problems, particularly during summer, which is detrimental for passenger comfort and health. Geothermal systems could serve as one of the potential green energy efficient cooling solutions, compared to energy intensive conventional cooling. The waste heat of the subway tunnel can be harnessed, to provide heating to residential and commercial blocks above the tunnels. The climate of a representative section of the London Underground{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}s (LU) Central Line (CL) is modeled using a 1D Modelica based software called IDA Tunnel. A 3D Comsol model that includes geothermal vertical boreholes on the tunnel sides is developed to asses their potential in cooling the LU tunnels and platforms. The IDA and Comsol models are co- simulated through exchanging boundary outer tunnel wall temperature information inorder to model the transient interactions between the boreholes and the tunnel and platform environment.}\n}\n\n
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\n Old and deep subway lines suffer from overheating problems, particularly during summer, which is detrimental for passenger comfort and health. Geothermal systems could serve as one of the potential green energy efficient cooling solutions, compared to energy intensive conventional cooling. The waste heat of the subway tunnel can be harnessed, to provide heating to residential and commercial blocks above the tunnels. The climate of a representative section of the London Undergroundï¿$\\frac{1}{2}$s (LU) Central Line (CL) is modeled using a 1D Modelica based software called IDA Tunnel. A 3D Comsol model that includes geothermal vertical boreholes on the tunnel sides is developed to asses their potential in cooling the LU tunnels and platforms. The IDA and Comsol models are co- simulated through exchanging boundary outer tunnel wall temperature information inorder to model the transient interactions between the boreholes and the tunnel and platform environment.\n
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\n \n\n \n \n \n \n \n \n Parameterisation of Internal Loads in Assessment of Building Energy Performance.\n \n \n \n \n\n\n \n Ward, R. M., Choudhary, R., Heo, Y., & Rysanek, A.\n\n\n \n\n\n\n In 14th International Conference of IBPSA, Building Simulation 2015, pages 2881–2888, Hyderabad, 2015. IBPSA\n \n\n\n\n
\n\n\n\n \n \n \"ParameterisationPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ward_parameterisation_2015,\n  title = {Parameterisation of Internal Loads in Assessment of Building Energy Performance},\n  booktitle = {14th {{International Conference}} of {{IBPSA}}, {{Building Simulation}} 2015},\n  author = {Ward, Rebecca Mary and Choudhary, Ruchi and Heo, Yeonsook and Rysanek, Adam},\n  year = {2015},\n  pages = {2881--2888},\n  publisher = {IBPSA},\n  address = {Hyderabad},\n  url = {http://www.bs2015.in/files/BS2015_Proceeding.pdf},\n  abstract = {In a computational building energy model internal loads are characterized by user-defined peak values, multiplied by diversity factors that simulate the typical daily change in use. For an existing building, while a detailed energy audit may be undertaken, attaining accurate internal load profiles for every space of the building can be prohibitive. In reality, the variation of internal loads over time is inherently stochastic. In order to develop a stochastic model of building operations, a number of studies have proposed parameterisations that incorporate some estimation of variability, with different assumptions and levels of complexity. This paper aims to examine potential models and thereby identify possible parameterisations for a stochastic model of internal loads in a building with quantification of uncertainties in inputs.},\n  project = {b-bem}\n}\n\n
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\n In a computational building energy model internal loads are characterized by user-defined peak values, multiplied by diversity factors that simulate the typical daily change in use. For an existing building, while a detailed energy audit may be undertaken, attaining accurate internal load profiles for every space of the building can be prohibitive. In reality, the variation of internal loads over time is inherently stochastic. In order to develop a stochastic model of building operations, a number of studies have proposed parameterisations that incorporate some estimation of variability, with different assumptions and levels of complexity. This paper aims to examine potential models and thereby identify possible parameterisations for a stochastic model of internal loads in a building with quantification of uncertainties in inputs.\n
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\n \n\n \n \n \n \n \n Importance Analysis and Meta-Model Construction with Correlated Variables in Evaluation of Thermal Performance of Campus Buildings.\n \n \n \n\n\n \n Tian, W., Choudhary, R., Augenbroe, G., & Lee, S. H.\n\n\n \n\n\n\n Building and Environment, 92: 61–74. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{tian_importance_2015,\n  title = {Importance Analysis and Meta-Model Construction with Correlated Variables in Evaluation of Thermal Performance of Campus Buildings},\n  author = {Tian, W. and Choudhary, R. and Augenbroe, G. and Lee, S. H.},\n  year = {2015},\n  journal = {Building and Environment},\n  volume = {92},\n  pages = {61--74},\n  doi = {10.1016/j.buildenv.2015.04.021},\n  abstract = {Statistical energy modelling \\&amp; analysis of building stock is becoming mainstream in the context of city or district scale analysis of energy saving measures. A common aspect in such analyses is that there is generally a set of key explanatory variables {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} or the main inputs {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} that are statistically related to a quantity of interest (end-use energy or CO2). In the context of energy use in buildings, it is not uncommon that the explanatory variables may be correlated. However, there has been little discussion about the correlated variables in building stock research. This paper uses a set of campus buildings as a demonstrative case study to investigate the application of variable importance and meta-model construction in the case of correlated inputs when quantifying energy demand of a building stock. The variable importance analysis can identify key factors that explain energy consumption of a building stock. To this end, it is necessary to apply methods suitable for correlated inputs because the observational data (inputs) of buildings are usually correlated. For constructing statistical energy meta-models, two types of regression models are used: linear and non-parametric models. The results indicate that the linear models perform well compared to the complicated non-parametric models in this case. In addition, a simple transformation of the response, commonly used in linear regression, can improve predictive performance of both the linear and non-parametric models.}\n}\n\n
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\n Statistical energy modelling & analysis of building stock is becoming mainstream in the context of city or district scale analysis of energy saving measures. A common aspect in such analyses is that there is generally a set of key explanatory variables ï¿$\\frac{1}{2}$ or the main inputs ï¿$\\frac{1}{2}$ that are statistically related to a quantity of interest (end-use energy or CO2). In the context of energy use in buildings, it is not uncommon that the explanatory variables may be correlated. However, there has been little discussion about the correlated variables in building stock research. This paper uses a set of campus buildings as a demonstrative case study to investigate the application of variable importance and meta-model construction in the case of correlated inputs when quantifying energy demand of a building stock. The variable importance analysis can identify key factors that explain energy consumption of a building stock. To this end, it is necessary to apply methods suitable for correlated inputs because the observational data (inputs) of buildings are usually correlated. For constructing statistical energy meta-models, two types of regression models are used: linear and non-parametric models. The results indicate that the linear models perform well compared to the complicated non-parametric models in this case. In addition, a simple transformation of the response, commonly used in linear regression, can improve predictive performance of both the linear and non-parametric models.\n
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\n \n\n \n \n \n \n \n Thermal Modeling and Parametric Analysis of Underground Rail Systems.\n \n \n \n\n\n \n Mortada, A., Choudhary, R., & Soga, K.\n\n\n \n\n\n\n Energy Procedia, 78: 2262–2267. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{mortada_thermal_2015,\n  title = {Thermal Modeling and Parametric Analysis of Underground Rail Systems},\n  author = {Mortada, A. and Choudhary, R. and Soga, K.},\n  year = {2015},\n  journal = {Energy Procedia},\n  volume = {78},\n  pages = {2262--2267},\n  doi = {10.1016/j.egypro.2015.11.362},\n  abstract = {The climate of a representative section of a subway station is modeled using a 1-dimensional Modelica based software called IDA Tunnel. Station building maps, rolling stock schematics, ventilation rates, and passenger traffic information are used to achieve a near realistic model of the London Underground's Central Line, as a representative case study. The system's heat sources and sinks are identified, and the model is calibrated using onsite temperature sensor data in the station platforms and tunnels. A parametric analysis is performed on the system's heat sources and sinks to identify the key factors that influence the subway station's climate. Results show that having low outer wall tunnel temperatures can be most effective in lowering the temperatures during peak periods, followed by regenerative braking and increased ventilation rates. These results can allow analysis of alternative cooling methods under future train and passenger traffic scenarios on the passengers{\\"i}{\\textquestiondown}{$\\frac{1}{2}$} transient thermal comfort in subway stations.}\n}\n\n
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\n The climate of a representative section of a subway station is modeled using a 1-dimensional Modelica based software called IDA Tunnel. Station building maps, rolling stock schematics, ventilation rates, and passenger traffic information are used to achieve a near realistic model of the London Underground's Central Line, as a representative case study. The system's heat sources and sinks are identified, and the model is calibrated using onsite temperature sensor data in the station platforms and tunnels. A parametric analysis is performed on the system's heat sources and sinks to identify the key factors that influence the subway station's climate. Results show that having low outer wall tunnel temperatures can be most effective in lowering the temperatures during peak periods, followed by regenerative braking and increased ventilation rates. These results can allow analysis of alternative cooling methods under future train and passenger traffic scenarios on the passengersï¿$\\frac{1}{2}$ transient thermal comfort in subway stations.\n
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\n \n\n \n \n \n \n \n Comparative Study on Machine Learning for Urban Building Energy Analysis.\n \n \n \n\n\n \n Wei, L., Tian, W., Silva, E. A., Choudhary, R., Meng, Q. X., & Yang, S.\n\n\n \n\n\n\n Procedia Engineering, 121: 285–292. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wei_comparative_2015,\n  title = {Comparative Study on Machine Learning for Urban Building Energy Analysis},\n  author = {Wei, L. and Tian, W. and Silva, E. A. and Choudhary, R. and Meng, Q. X. and Yang, S.},\n  year = {2015},\n  journal = {Procedia Engineering},\n  volume = {121},\n  pages = {285--292},\n  doi = {10.1016/j.proeng.2015.08.1070},\n  abstract = {There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age groups, etc. Six machine learning methods are two linear approaches (full linear and Lasso) and four non-parametric methods (MARS multivariate adaptive regression spline, SVM support vector machine, bagging MARS, and boosting). The results indicate that all the four non-parametric models outperform two linear models. The SVM models perform the best among these models for both gas and electricity. The bagging MARS performs only a little worse than the SVM for gas use prediction. The Lasso model has similar predictive capability to the full linear model in this case.}\n}\n\n
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\n There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age groups, etc. Six machine learning methods are two linear approaches (full linear and Lasso) and four non-parametric methods (MARS multivariate adaptive regression spline, SVM support vector machine, bagging MARS, and boosting). The results indicate that all the four non-parametric models outperform two linear models. The SVM models perform the best among these models for both gas and electricity. The bagging MARS performs only a little worse than the SVM for gas use prediction. The Lasso model has similar predictive capability to the full linear model in this case.\n
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\n \n\n \n \n \n \n \n Variable Importance Analysis for Urban Building Energy Assessment in the Presence of Correlated Factors.\n \n \n \n\n\n \n Yang, S., Tian, W., Heo, Y., Meng, Q., & Wei, L.\n\n\n \n\n\n\n Procedia Engineering, 121: 277–284. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{yang_variable_2015,\n  title = {Variable Importance Analysis for Urban Building Energy Assessment in the Presence of Correlated Factors},\n  author = {Yang, S. and Tian, W. and Heo, Y. and Meng, Q. and Wei, L.},\n  year = {2015},\n  journal = {Procedia Engineering},\n  volume = {121},\n  pages = {277--284},\n  doi = {10.1016/j.proeng.2015.08.1069},\n  abstract = {It is becoming urgent to thoroughly understand characteristics of energy use in order to reduce energy use in urban areas. When assessing energy performance in urban buildings, it is likely that explanatory variables are correlated if considering both physical conditions and social economic factors. This research applied three variable importance methods, including Genizi, CAR (Correlation-Adjusted marginal coRrelation), PCC (partial correlation coefficient), to identify key factors from 30 highly correlated variables in London. The results indicate that the land area for domestic buildings is the only dominant variable influencing gas use, while electricity consumption is more affected by the number of electricity meters for Economy 7 (a differential electricity tariff according to the time of day) and the number of households allocated to higher council tax band in London. Moreover, it is confirmed that the SRC (standardized regression coefficient), a commonly used method in building energy analysis, is not suitable for the correlated factors in urban energy assessment.}\n}\n\n
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\n\n\n
\n It is becoming urgent to thoroughly understand characteristics of energy use in order to reduce energy use in urban areas. When assessing energy performance in urban buildings, it is likely that explanatory variables are correlated if considering both physical conditions and social economic factors. This research applied three variable importance methods, including Genizi, CAR (Correlation-Adjusted marginal coRrelation), PCC (partial correlation coefficient), to identify key factors from 30 highly correlated variables in London. The results indicate that the land area for domestic buildings is the only dominant variable influencing gas use, while electricity consumption is more affected by the number of electricity meters for Economy 7 (a differential electricity tariff according to the time of day) and the number of households allocated to higher council tax band in London. Moreover, it is confirmed that the SRC (standardized regression coefficient), a commonly used method in building energy analysis, is not suitable for the correlated factors in urban energy assessment.\n
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\n \n\n \n \n \n \n \n Simulation of Plants in Buildings; Incorporating Plant-Air Interactions in Building Energy Simulation.\n \n \n \n\n\n \n Ward, R., Choudhary, R., Cundy, C., Johnson, G., & McRobie, A.\n\n\n \n\n\n\n In 14th International Conference of IBPSA-building Simulation 2015, BS 2015, Conference Proceedings, pages 2256–2263, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ward2015simulation,\n  title = {Simulation of Plants in Buildings; Incorporating Plant-{{Air}} Interactions in Building Energy Simulation},\n  booktitle = {14th International Conference of {{IBPSA-building}} Simulation 2015, {{BS}} 2015, Conference Proceedings},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Cundy, Christopher and Johnson, George and McRobie, Allan},\n  year = {2015},\n  pages = {2256--2263}\n}\n\n
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\n \n\n \n \n \n \n \n Data-Driven Model for Rooftop Excess Electricity Generation.\n \n \n \n\n\n \n Kiguchi, Y., Heo, e., & Choudhary, R.\n\n\n \n\n\n\n In Proceedings of the 14th IBPSA Conference, Hyderabad, India, pages 7–9, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{kiguchi2015data,\n  title = {Data-Driven Model for Rooftop Excess Electricity Generation},\n  booktitle = {Proceedings of the 14th {{IBPSA}} Conference, Hyderabad, India},\n  author = {Kiguchi, Yohei and Heo, {\\relax YS} and Choudhary, Ruchi},\n  year = {2015},\n  pages = {7--9}\n}\n\n
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\n \n\n \n \n \n \n \n Sensitivity of Mesoscale Models to Scale Dependent UCP Inputs: An Example from Urban Energy Demand.\n \n \n \n\n\n \n Neophytou, M., Mouzourides, P., Kyprianou, A., Choudhary, R., & Ching, J\n\n\n \n\n\n\n In 9th International Conference on Urban Climate Jointly with 12th Symposium on the Urban Environment, Toulouse, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{neophytou2015sensitivity,\n  title = {Sensitivity of Mesoscale Models to Scale Dependent {{UCP}} Inputs: An Example from Urban Energy Demand},\n  booktitle = {9th International Conference on Urban Climate Jointly with 12th Symposium on the Urban Environment, Toulouse},\n  author = {Neophytou, Marina and Mouzourides, Petros and Kyprianou, Andreas and Choudhary, Ruchi and Ching, J},\n  year = {2015}\n}\n\n
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\n \n\n \n \n \n \n \n High Resolution Energy Simulations at City Scale.\n \n \n \n\n\n \n Tian, W., Rysanek, A., Choudhary, R., & Heo, Y.\n\n\n \n\n\n\n In 14th Conference of International Building Performance Simulation Association, BS 2015, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{tian2015high,\n  title = {High Resolution Energy Simulations at City Scale},\n  booktitle = {14th Conference of International Building Performance Simulation Association, {{BS}} 2015},\n  author = {Tian, Wei and Rysanek, Adam and Choudhary, Ruchi and Heo, Yeonsook},\n  year = {2015}\n}\n\n
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\n  \n 2014\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Shallow Geothermal Energy Application with GSHPs at City Scale: Study on the City of Westminster.\n \n \n \n \n\n\n \n Zhang, Y., Soga, K., & Choudhary, R.\n\n\n \n\n\n\n Gï¿$\\frac{1}{2}$otechnique Letters, 4(2): 125–131. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ShallowPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1680/geolett.13.00061,\n  title = {Shallow Geothermal Energy Application with {{GSHPs}} at City Scale: Study on the {{City}} of {{Westminster}}},\n  author = {Zhang, Y. and Soga, K. and Choudhary, R.},\n  year = {2014},\n  journal = {G{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}otechnique Letters},\n  volume = {4},\n  number = {2},\n  eprint = {http://dx.doi.org/10.1680/geolett.13.00061},\n  pages = {125--131},\n  doi = {10.1680/geolett.13.00061},\n  url = {http://dx.doi.org/10.1680/geolett.13.00061},\n  abstract = {Geothermal energy is an efficient low carbon solution for the heating and cooling of buildings. For many megacities such as London and Beijing, the amount of energy that can be stored in the urban local subsurface is greater than their annual heating and cooling demands. The ground source heat pump (GSHP) system {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} a shallow geothermal technology that provides heating and cooling for buildings by continuously replenishing the energy in the subsurface {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} has been used increasingly in recent years, but its application has been generally limited to single buildings. In this study, a geographic information system-based simulation model was developed to estimate how many GSHPs could be installed at the city scale without losing control of the ground thermal capacity and to evaluate the degree to which such a system could contribute to the energy demands of buildings in a city. The model was built by embedding a Python-based GSHP design code into ArcGIS software and was trialled on the City of Westminster, a borough in London, UK, as a case study under the two scenarios of boreholes placed under buildings and boreholes around buildings. Under both scenarios, the model produced borehole allocation maps and ratio of capacity to demand maps. The results show that a large proportion of buildings could support their own heating demands through GSHPs and, through a well-organised district heating system, GSHPs may be used efficiently to satisfy heating demands throughout an urban area.}\n}\n\n
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\n Geothermal energy is an efficient low carbon solution for the heating and cooling of buildings. For many megacities such as London and Beijing, the amount of energy that can be stored in the urban local subsurface is greater than their annual heating and cooling demands. The ground source heat pump (GSHP) system ï¿$\\frac{1}{2}$ a shallow geothermal technology that provides heating and cooling for buildings by continuously replenishing the energy in the subsurface ï¿$\\frac{1}{2}$ has been used increasingly in recent years, but its application has been generally limited to single buildings. In this study, a geographic information system-based simulation model was developed to estimate how many GSHPs could be installed at the city scale without losing control of the ground thermal capacity and to evaluate the degree to which such a system could contribute to the energy demands of buildings in a city. The model was built by embedding a Python-based GSHP design code into ArcGIS software and was trialled on the City of Westminster, a borough in London, UK, as a case study under the two scenarios of boreholes placed under buildings and boreholes around buildings. Under both scenarios, the model produced borehole allocation maps and ratio of capacity to demand maps. The results show that a large proportion of buildings could support their own heating demands through GSHPs and, through a well-organised district heating system, GSHPs may be used efficiently to satisfy heating demands throughout an urban area.\n
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\n \n\n \n \n \n \n \n \n Uncertainty Quantification of Microclimate Variables in Building Energy Models.\n \n \n \n \n\n\n \n Sun, Y., Heo, Y., Tan, M., Xie, H., Wu, C. J., & Augenbroe, G.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 7(1): 17–32. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"UncertaintyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2012.757368,\n  title = {Uncertainty Quantification of Microclimate Variables in Building Energy Models},\n  author = {Sun, Yuming and Heo, Yeonsook and Tan, Matthias and Xie, Huizhi and Wu, C.F. Jeff and Augenbroe, Godfried},\n  year = {2014},\n  journal = {Journal of Building Performance Simulation},\n  volume = {7},\n  number = {1},\n  eprint = {http://dx.doi.org/10.1080/19401493.2012.757368},\n  pages = {17--32},\n  doi = {10.1080/19401493.2012.757368},\n  url = {http://dx.doi.org/10.1080/19401493.2012.757368},\n  abstract = {The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.}\n}\n\n
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\n The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.\n
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\n \n\n \n \n \n \n \n \n Influence of District Features on Energy Consumption in Non-Domestic Buildings.\n \n \n \n \n\n\n \n Choudhary, R., & Tian, W.\n\n\n \n\n\n\n Building Research & Information, 42(1): 32–46. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"InfluencePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/09613218.2014.832559,\n  title = {Influence of District Features on Energy Consumption in Non-Domestic Buildings},\n  author = {Choudhary, R. and Tian, W.},\n  year = {2014},\n  journal = {Building Research \\&amp; Information},\n  volume = {42},\n  number = {1},\n  eprint = {http://dx.doi.org/10.1080/09613218.2014.832559},\n  pages = {32--46},\n  doi = {10.1080/09613218.2014.832559},\n  url = {http://dx.doi.org/10.1080/09613218.2014.832559},\n  abstract = {The spatial variability of gas consumption is investigated in non-domestic buildings across districts of Greater London, UK. It is argued that the energy consumption of a building is to some extent influenced by where the building is located in a city, due to contextual features of its own district as well as those of neighbouring districts. Using Bayesian spatial models, the analysis suggests the energy consumption due to the influence of district features can be quantified and dissociated from the energy consumption associated with the physical features and operational characteristics of the buildings. An important distinction is made between extrinsic values of energy consumption (district features) and intrinsic values (building characteristics, management and operation). The results indicate that 90\\% of the mean value of extrinsic gas consumption across districts in London is between {\\"i}{\\textquestiondown}{$\\frac{1}{2}$}??42 and 87 kWh/m2. Buildings located in districts that have positive values of extrinsic gas consumption consume more gas over and above their expected intrinsic value of gas consumption. The novel features of this study are in the quantification and propagation of district-scale features to their influence on buildings, and the reduction in uncertainties around the mean value of gas consumed by different building types.}\n}\n\n
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\n The spatial variability of gas consumption is investigated in non-domestic buildings across districts of Greater London, UK. It is argued that the energy consumption of a building is to some extent influenced by where the building is located in a city, due to contextual features of its own district as well as those of neighbouring districts. Using Bayesian spatial models, the analysis suggests the energy consumption due to the influence of district features can be quantified and dissociated from the energy consumption associated with the physical features and operational characteristics of the buildings. An important distinction is made between extrinsic values of energy consumption (district features) and intrinsic values (building characteristics, management and operation). The results indicate that 90% of the mean value of extrinsic gas consumption across districts in London is between ï¿$\\frac{1}{2}$??42 and 87 kWh/m2. Buildings located in districts that have positive values of extrinsic gas consumption consume more gas over and above their expected intrinsic value of gas consumption. The novel features of this study are in the quantification and propagation of district-scale features to their influence on buildings, and the reduction in uncertainties around the mean value of gas consumed by different building types.\n
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\n \n\n \n \n \n \n \n Measurement and Verification of Building Systems under Uncertain Data: A Gaussian Process Modeling Approach.\n \n \n \n\n\n \n Burkhart, M. C., Heo, Y., & Zavala, V. M.\n\n\n \n\n\n\n Energy and Buildings, 75: 189–198. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{burkhart_measurement_2014,\n  title = {Measurement and Verification of Building Systems under Uncertain Data: A {{Gaussian}} Process Modeling Approach},\n  author = {Burkhart, M. C. and Heo, Y. and Zavala, V. M.},\n  year = {2014},\n  journal = {Energy and Buildings},\n  volume = {75},\n  pages = {189--198},\n  doi = {10.1016/j.enbuild.2014.01.048},\n  abstract = {Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M\\&amp;V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M\\&amp;V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.}\n}\n\n
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\n Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.\n
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\n \n\n \n \n \n \n \n Searching for the Distinctive Signature of a City in Atmospheric Modelling: Could the Multi-Resolution Analysis (MRA) Provide the DNA of a City?.\n \n \n \n\n\n \n Mouzourides, P., Kyprianou, A., Brown, M. J, Carissimo, B., Choudhary, R., & Neophytou, M. K.\n\n\n \n\n\n\n Urban Climate, 10: 447–475. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{mouzourides2014searching,\n  title = {Searching for the Distinctive Signature of a City in Atmospheric Modelling: {{Could}} the {{Multi-Resolution Analysis}} ({{MRA}}) Provide the {{DNA}} of a City?},\n  author = {Mouzourides, Petros and Kyprianou, Andreas and Brown, Michael J and Carissimo, Bertrand and Choudhary, Ruchi and Neophytou, Marina K-A},\n  year = {2014},\n  journal = {Urban Climate},\n  volume = {10},\n  pages = {447--475},\n  publisher = {Elsevier}\n}\n\n
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\n  \n 2013\n \n \n (18)\n \n \n
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\n \n\n \n \n \n \n \n MPC-based Appliance Scheduling for Residential Building Energy Management Controller.\n \n \n \n\n\n \n Chen, C., Wang, J., Heo, Y., & Kishore, S.\n\n\n \n\n\n\n IEEE Transactions on Smart Grid, 4(3): 1401–1410. September 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{6575202,\n  title = {{{MPC-based}} Appliance Scheduling for Residential Building Energy Management Controller},\n  author = {Chen, C. and Wang, J. and Heo, Y. and Kishore, S.},\n  year = {2013},\n  month = sep,\n  journal = {IEEE Transactions on Smart Grid},\n  volume = {4},\n  number = {3},\n  pages = {1401--1410},\n  issn = {1949-3053},\n  doi = {10.1109/TSG.2013.2265239},\n  abstract = {This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated.},\n  keywords = {appliance scheduling scheme,Building,building management systems,Buildings,Delays,Electricity,energy cost savings,energy management controller,energy management systems,finite-horizon scheduling optimization problems,Home appliances,low-price time periods,model predictive control,MPC,MPC method,nonthermal appliance scheduling,operation dependence,optimisation,optimization,Optimization,PMV index,Power demand,predicted mean vote,predictive control,price variation,pricing,residential building energy management controllers,scheduling,smart grid,smart power grids,thermal mass storage,Thermodynamics,time-varying retail pricing,time-varying systems,two-way communication infrastructure}\n}\n\n
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\n This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated.\n
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\n \n\n \n \n \n \n \n \n Air Quality and Public Health Impacts of \\p̌hantom\\UKp̌hantom\\\\ Airports. Part II: Impacts and Policy Assessment.\n \n \n \n \n\n\n \n Yim, S. H., Stettler, M. E., & Barrett, S. R.\n\n\n \n\n\n\n Atmospheric Environment, 67: 184–192. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"AirPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Yim2013184,\n  title = {Air Quality and Public Health Impacts of \\{\\vphantom\\}{{UK}}\\vphantom\\{\\} Airports. {{Part II}}: {{Impacts}} and Policy Assessment},\n  author = {Yim, Steve H.L. and Stettler, Marc E.J. and Barrett, Steven R.H.},\n  year = {2013},\n  journal = {Atmospheric Environment},\n  volume = {67},\n  pages = {184--192},\n  issn = {1352-2310},\n  doi = {http://dx.doi.org/10.1016/j.atmosenv.2012.10.017},\n  url = {http://www.sciencedirect.com/science/article/pii/S1352231012009818},\n  abstract = {The potential adverse human health impacts of emissions from \\{UK\\} airports have become a significant issue of public concern. We produce an inventory of \\{UK\\} airport emissions {\\"i}{\\textquestiondown}{$\\frac{1}{2}$}?? including emissions from aircraft landing and takeoff operations, aircraft auxiliary power units (APUs) and ground support equipment (GSE) {\\"i}{\\textquestiondown}{$\\frac{1}{2}$}?? with quantified uncertainty. Emissions due to more than 95},\n  keywords = {Air quality,Aircraft,Airports,Aviation,Dispersion,Emissions,Particulate matter}\n}\n\n
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\n The potential adverse human health impacts of emissions from \\UK\\ airports have become a significant issue of public concern. We produce an inventory of \\UK\\ airport emissions ï¿$\\frac{1}{2}$?? including emissions from aircraft landing and takeoff operations, aircraft auxiliary power units (APUs) and ground support equipment (GSE) ï¿$\\frac{1}{2}$?? with quantified uncertainty. Emissions due to more than 95\n
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\n \n\n \n \n \n \n \n \n Rapid Estimation of Global Civil Aviation Emissions with Uncertainty Quantification.\n \n \n \n \n\n\n \n Simone, N. W., Stettler, M. E., & Barrett, S. R.\n\n\n \n\n\n\n Transportation Research Part D: Transport and Environment, 25: 33–41. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"RapidPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Simone201333,\n  title = {Rapid Estimation of Global Civil Aviation Emissions with Uncertainty Quantification},\n  author = {Simone, Nicholas W. and Stettler, Marc E.J. and Barrett, Steven R.H.},\n  year = {2013},\n  journal = {Transportation Research Part D: Transport and Environment},\n  volume = {25},\n  pages = {33--41},\n  issn = {1361-9209},\n  doi = {http://dx.doi.org/10.1016/j.trd.2013.07.001},\n  url = {http://www.sciencedirect.com/science/article/pii/S1361920913001028},\n  abstract = {Abstract In this paper we describe the methods used to develop the open source Aviation Emissions Inventory Code and produce a global emissions inventory for scheduled civil aviation, with quantified uncertainty. We estimate that in 2005, scheduled civil aviation was responsible for 180.6 Tg of fuel burn, which agrees to within 4},\n  keywords = {Aircraft emissions,Aviation Emissions Inventory Code,Aviation fuel burn}\n}\n\n
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\n Abstract In this paper we describe the methods used to develop the open source Aviation Emissions Inventory Code and produce a global emissions inventory for scheduled civil aviation, with quantified uncertainty. We estimate that in 2005, scheduled civil aviation was responsible for 180.6 Tg of fuel burn, which agrees to within 4\n
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\n \n\n \n \n \n \n \n Analysis and Optimisation of Retrofit Energy Supply Strategy across a Diverse Urban Building Portfolio.\n \n \n \n\n\n \n Ward, R. M., Mortada, A., Omu, K., Rysanek, A. M., Rainsford, C., & Choudhary, R.\n\n\n \n\n\n\n In Building Simulation 2013, pages 1256–1263, 2013. http://www.ibpsa.org\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ward2013analysis,\n  title = {Analysis and Optimisation of Retrofit Energy Supply Strategy across a Diverse Urban Building Portfolio},\n  booktitle = {Building Simulation 2013},\n  author = {Ward, Rebecca Mary and Mortada, Adnan and Omu, Keno and Rysanek, Adam Martin and Rainsford, Clare and Choudhary, Ruchi},\n  year = {2013},\n  pages = {1256--1263},\n  publisher = {http://www.ibpsa.org},\n  abstract = {This paper presents a study in support of decision making for building retrofit and energy supply strategy at the Royal Botanic Gardens, Kew in southwest London, England. The study considers the issues that affect simulation at the building scale specific to this site, in particular simulation of heat flow in botanical glasshouses, retrofit of heritage structures and simulation of power load for buildings with high equipment density. In addition, the study considers the potential benefits to be gained from energy microgeneration and supply at the district scale, investigating supply optimisation for a cluster of buildings within the Kew site.}\n}\n\n
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\n This paper presents a study in support of decision making for building retrofit and energy supply strategy at the Royal Botanic Gardens, Kew in southwest London, England. The study considers the issues that affect simulation at the building scale specific to this site, in particular simulation of heat flow in botanical glasshouses, retrofit of heritage structures and simulation of power load for buildings with high equipment density. In addition, the study considers the potential benefits to be gained from energy microgeneration and supply at the district scale, investigating supply optimisation for a cluster of buildings within the Kew site.\n
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\n \n\n \n \n \n \n \n Urban-Scale Energy Modeling of Food Supermarket Considering Uncertainty.\n \n \n \n\n\n \n Yamaguchi, Y., Choudhary, R, Booth, A, Suzuki, Y, & Shimoda, Y.\n\n\n \n\n\n\n The proceedings of BS. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{yamaguchi2013urban,\n  title = {Urban-Scale Energy Modeling of Food Supermarket Considering Uncertainty},\n  author = {Yamaguchi, Yohei and Choudhary, R and Booth, A and Suzuki, Y and Shimoda, Yoshiyuki},\n  year = {2013},\n  journal = {The proceedings of BS},\n  abstract = {This paper proposes a methodology to develop an urban scale model of energy use of buildings. this methodology addresses the diversity in energy use through two approaches: classification of the building stock and archetype modelling; supported by Bayesian calibration. The Bayesian approach allows quantification of uncertainties in model input parameters. We designed a hierarchical calibration so that uncertain input model parameters are calibrated for different time resolutions of analysis. We validated the calibration process by applying it to a food supermarket building stock in a region as a case study. The results generally showed that the proposed approach enables urban scale models to take into account not only the overall characteristics of the building stock represented by annual energy consumption but also the influence of meteorological conditions shown in the variation of weekly energy consumption}\n}\n\n
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\n This paper proposes a methodology to develop an urban scale model of energy use of buildings. this methodology addresses the diversity in energy use through two approaches: classification of the building stock and archetype modelling; supported by Bayesian calibration. The Bayesian approach allows quantification of uncertainties in model input parameters. We designed a hierarchical calibration so that uncertain input model parameters are calibrated for different time resolutions of analysis. We validated the calibration process by applying it to a food supermarket building stock in a region as a case study. The results generally showed that the proposed approach enables urban scale models to take into account not only the overall characteristics of the building stock represented by annual energy consumption but also the influence of meteorological conditions shown in the variation of weekly energy consumption\n
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\n \n\n \n \n \n \n \n Cost-Effective Measurement and Verification Method for Determining Energy Savings under Uncertainty.\n \n \n \n\n\n \n Heo, Y., Graziano, D. J, Zavala, V. M, Dickinson, P., Kamrath, M., & Kirshenbaum, M.\n\n\n \n\n\n\n ASHRAE Transactions, 119: EE1. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{heo2013cost,\n  title = {Cost-Effective Measurement and Verification Method for Determining Energy Savings under Uncertainty},\n  author = {Heo, Yeonsook and Graziano, Diane J and Zavala, Victor M and Dickinson, Peter and Kamrath, Mark and Kirshenbaum, Marvin},\n  year = {2013},\n  journal = {ASHRAE Transactions},\n  volume = {119},\n  pages = {EE1},\n  publisher = {{American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc.}},\n  abstract = {In this paper, for measurement and verification (M\\&amp;V) of energy savings in buildings, the authors propose an approach based on Gaussian Process (GP) modeling that can represent nonlinear energy behavior, multivariable interactions and time correlations while quantifying uncertainty associated with predictions. They applied the GP modeling to determine the energy savings from BuildingIQ's energy management system deployed at the Advanced Photon Source Office building at Argonne. The case study demonstrates the potential strengths of GP models for M\\&amp;V and explores the importance of dataset characteristics and explanatory variables for the reliability of analysis results. The case study illustrates the capability of GP modeling to predict hourly dynamic behavior, exploiting the possibility to reduce uncertainty in energy-use predictions using measured data with finer time resolutions. The proposed M\\&amp;V approach is amendable to automation in energy management systems and continuous monitoring of energy performance.}\n}\n\n
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\n In this paper, for measurement and verification (M&V) of energy savings in buildings, the authors propose an approach based on Gaussian Process (GP) modeling that can represent nonlinear energy behavior, multivariable interactions and time correlations while quantifying uncertainty associated with predictions. They applied the GP modeling to determine the energy savings from BuildingIQ's energy management system deployed at the Advanced Photon Source Office building at Argonne. The case study demonstrates the potential strengths of GP models for M&V and explores the importance of dataset characteristics and explanatory variables for the reliability of analysis results. The case study illustrates the capability of GP modeling to predict hourly dynamic behavior, exploiting the possibility to reduce uncertainty in energy-use predictions using measured data with finer time resolutions. The proposed M&V approach is amendable to automation in energy management systems and continuous monitoring of energy performance.\n
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\n \n\n \n \n \n \n \n Post-Occupancy Assessment of Thermal-Pile and Open-Well Ground Source Heat Pump (GSHP) System-Case Study.\n \n \n \n\n\n \n Garber, D.\n\n\n \n\n\n\n ASHRAE Transactions, 119: W1. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{garber2013post,\n  title = {Post-Occupancy Assessment of Thermal-Pile and Open-Well Ground Source Heat Pump ({{GSHP}}) System-Case Study},\n  author = {Garber, Denis},\n  year = {2013},\n  journal = {ASHRAE Transactions},\n  volume = {119},\n  pages = {W1},\n  publisher = {{American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc.}},\n  abstract = {This paper presents a case study of a 1.5 MW (426 ton) capacity Ground Source Heat Pumps (GSHP)system installed in the One New Change retail development in the UK. The system includes 192 thermal-piles underneath the building foundations and two open-well heat exchangers. The GSHP system was simulated using the TRNSYS energy simulation platform. A conventional borehole model based on the 'Duct Ground Heat Storage Model' was used to model the thermal-piles. The results of the model matched to within {\\"i}{\\textquestiondown}{$\\frac{1}{2}$}11}\n}\n\n
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\n This paper presents a case study of a 1.5 MW (426 ton) capacity Ground Source Heat Pumps (GSHP)system installed in the One New Change retail development in the UK. The system includes 192 thermal-piles underneath the building foundations and two open-well heat exchangers. The GSHP system was simulated using the TRNSYS energy simulation platform. A conventional borehole model based on the 'Duct Ground Heat Storage Model' was used to model the thermal-piles. The results of the model matched to within ï¿$\\frac{1}{2}$11\n
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\n \n\n \n \n \n \n \n Simulation of Thermal Performance and Retrofit of a Historic Greenhouse.\n \n \n \n\n\n \n Ward, R. M., Mortada, A., & Choudhary, R.\n\n\n \n\n\n\n CONTRIBUTIONS TO BUILDING PHYSICS,245–252. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ward2013simulation,\n  title = {Simulation of Thermal Performance and Retrofit of a Historic Greenhouse},\n  author = {Ward, Rebecca Mary and Mortada, Adnan and Choudhary, Ruchi},\n  year = {2013},\n  journal = {CONTRIBUTIONS TO BUILDING PHYSICS},\n  pages = {245--252},\n  abstract = {Typical building energy simulation programs do not adequately describe the physical processes of heat and mass transfer which occur in a greenhouse, primarily because they do not include the interaction of the plants with their environment. This is of concern when simulation studies are required in order to assess different options for improving the greenhouse thermal performance. A previous paper (Brown et al 2012) has described the development of a model for simulation of ornamental glasshouses. Further development, in order to represent more accurately the physical processes and assess their significance, is described in this paper. The development has been undertaken in support of retrofit analysis for the glasshouses at the Royal Botanic Gardens, Kew, in London, which present unique challenges in terms of their historic nature, construction details and design and have provided an opportunity to investigate the relative importance of different physical effects on the energy consumption. Within this context, the simulation has been used to explore options for improving greenhouse thermal performance.},\n  project = {GES}\n}\n\n
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\n Typical building energy simulation programs do not adequately describe the physical processes of heat and mass transfer which occur in a greenhouse, primarily because they do not include the interaction of the plants with their environment. This is of concern when simulation studies are required in order to assess different options for improving the greenhouse thermal performance. A previous paper (Brown et al 2012) has described the development of a model for simulation of ornamental glasshouses. Further development, in order to represent more accurately the physical processes and assess their significance, is described in this paper. The development has been undertaken in support of retrofit analysis for the glasshouses at the Royal Botanic Gardens, Kew, in London, which present unique challenges in terms of their historic nature, construction details and design and have provided an opportunity to investigate the relative importance of different physical effects on the energy consumption. Within this context, the simulation has been used to explore options for improving greenhouse thermal performance.\n
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\n \n\n \n \n \n \n \n \n Quantitative Risk Management for Energy Retrofit Projects.\n \n \n \n \n\n\n \n Heo, Y., Augenbroe, G., & Choudhary, R.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 6(4): 257–268. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"QuantitativePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{doi:10.1080/19401493.2012.706388,\n  title = {Quantitative Risk Management for Energy Retrofit Projects},\n  author = {Heo, Yeonsook and Augenbroe, Godfried and Choudhary, Ruchi},\n  year = {2013},\n  journal = {Journal of Building Performance Simulation},\n  volume = {6},\n  number = {4},\n  eprint = {http://dx.doi.org/10.1080/19401493.2012.706388},\n  pages = {257--268},\n  doi = {10.1080/19401493.2012.706388},\n  url = {http://dx.doi.org/10.1080/19401493.2012.706388},\n  abstract = {This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.}\n}\n\n
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\n This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.\n
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\n \n\n \n \n \n \n \n \n Decision Making under Uncertainty in the Retrofit Analysis of the \\p̌hantom\\UKp̌hantom\\\\ Housing Stock: Implications for the Green Deal.\n \n \n \n \n\n\n \n Booth, A., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 64: 292–308. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"DecisionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Booth2013292,\n  title = {Decision Making under Uncertainty in the Retrofit Analysis of the \\{\\vphantom\\}{{UK}}\\vphantom\\{\\} Housing Stock: {{Implications}} for the {{Green Deal}}},\n  author = {Booth, A.T. and Choudhary, R.},\n  year = {2013},\n  journal = {Energy and Buildings},\n  volume = {64},\n  pages = {292--308},\n  issn = {0378-7788},\n  doi = {http://dx.doi.org/10.1016/j.enbuild.2013.05.014},\n  url = {http://www.sciencedirect.com/science/article/pii/S0378778813002909},\n  abstract = {Abstract In order to reduce carbon emissions and alleviate fuel poverty, the \\{UK\\} Government has outlined proposals for a 'Green Deal' to help provide financing for the installation of cost-effective retrofit measures to the existing \\{UK\\} housing stock. However, the Green Deal proposals have the potential to generate financial risk, due to a possible overestimation of the energy savings arising from retrofit measures. This paper proposes a framework for handling the uncertainties associated with the prediction of these energy savings, as well as demonstrating how decisions can be made in the face of the uncertainties involved in the retrofit analysis of a housing stock. The proposed framework is applied to a case study set of dwellings and it is seen that a limited range of measures will be cost-effective under the Green Deal proposals for these dwellings; as a result, subsidies will be required if higher impact measures are to be considered viable. Finally, however, it is also seen that the monetary value of additional societal benefits, such as reduced carbon emissions and improved thermal comfort, is likely to more than outweigh the cost of any subsidies.},\n  project = {susdem},\n  keywords = {Bayesian,Decision making,Green Deal,Housing stock,Rebound effect,Residential energy,Retrofit analysis,Uncertainty}\n}\n\n
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\n Abstract In order to reduce carbon emissions and alleviate fuel poverty, the \\UK\\ Government has outlined proposals for a 'Green Deal' to help provide financing for the installation of cost-effective retrofit measures to the existing \\UK\\ housing stock. However, the Green Deal proposals have the potential to generate financial risk, due to a possible overestimation of the energy savings arising from retrofit measures. This paper proposes a framework for handling the uncertainties associated with the prediction of these energy savings, as well as demonstrating how decisions can be made in the face of the uncertainties involved in the retrofit analysis of a housing stock. The proposed framework is applied to a case study set of dwellings and it is seen that a limited range of measures will be cost-effective under the Green Deal proposals for these dwellings; as a result, subsidies will be required if higher impact measures are to be considered viable. Finally, however, it is also seen that the monetary value of additional societal benefits, such as reduced carbon emissions and improved thermal comfort, is likely to more than outweigh the cost of any subsidies.\n
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\n \n\n \n \n \n \n \n A Recursive Spatial Equilibrium Model for Planning Large-Scale Urban Change.\n \n \n \n\n\n \n Jin, Y., Echenique, M, & Hargreaves, A\n\n\n \n\n\n\n Environment and Planning B: Planning and Design, 40: 1027–1050. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jin_recursive_2013,\n  title = {A Recursive Spatial Equilibrium Model for Planning Large-Scale Urban Change},\n  author = {Jin, Ying and Echenique, M and Hargreaves, A},\n  year = {2013},\n  journal = {Environment and Planning B: Planning and Design},\n  volume = {40},\n  pages = {1027--1050},\n  doi = {10.1068/b39134}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Hierarchical Bayesian Framework for Calibrating Micro-Level Models with Macro-Level Data.\n \n \n \n \n\n\n \n Booth, A. T., Choudhary, R., & Spiegelhalter, D. J.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 6(4): 293–318. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{booth_hierarchical_2013,\n  title = {A Hierarchical {{Bayesian}} Framework for Calibrating Micro-Level Models with Macro-Level Data},\n  author = {Booth, A. T. and Choudhary, R. and Spiegelhalter, D. J.},\n  year = {2013},\n  journal = {Journal of Building Performance Simulation},\n  volume = {6},\n  number = {4},\n  pages = {293--318},\n  issn = {1940-1493},\n  doi = {10.1080/19401493.2012.723750},\n  url = {http://www.tandfonline.com/doi/abs/10.1080/19401493.2012.723750},\n  urldate = {2014-01-10TZ},\n  abstract = {Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level, at the district, urban, or national scale. This paper outlines a method for using macro-level data to calibrate micro-level models. A hierarchical framework is proposed, utilizing a combination of regression analysis and Bayesian inference. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types whilst quantifying uncertainty in both the empirical data and the generated energy estimates. Finally, the Bayesian regression method is validated and the use of the hierarchical Bayesian calibration framework is demonstrated.},\n  project = {susdem}\n}\n\n
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\n Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level, at the district, urban, or national scale. This paper outlines a method for using macro-level data to calibrate micro-level models. A hierarchical framework is proposed, utilizing a combination of regression analysis and Bayesian inference. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types whilst quantifying uncertainty in both the empirical data and the generated energy estimates. Finally, the Bayesian regression method is validated and the use of the hierarchical Bayesian calibration framework is demonstrated.\n
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\n \n\n \n \n \n \n \n \n Optimum Building Energy Retrofits under Technical and Economic Uncertainty.\n \n \n \n \n\n\n \n Rysanek, A. M., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 57: 324–337. February 2013.\n \n\n\n\n
\n\n\n\n \n \n \"OptimumPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{rysanek_optimum_2013,\n  title = {Optimum Building Energy Retrofits under Technical and Economic Uncertainty},\n  author = {Rysanek, A. M. and Choudhary, R.},\n  year = {2013},\n  month = feb,\n  journal = {Energy and Buildings},\n  volume = {57},\n  pages = {324--337},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2012.10.027},\n  url = {http://www.sciencedirect.com/science/article/pii/S0378778812005361},\n  urldate = {2014-01-10TZ},\n  abstract = {In a prior study, the authors showed that decomposing holistic, blackbox building energy models into discrete components can increase the computational efficiency of large-scale retrofit analysis. This paper presents an extension of that methodology to include an economic cost-benefit model. The entire framework now comprises an integrated modelling procedure for the simulation and optimisation of retrofit decisions for individual buildings. Potential decisions can range from the installation of demand-side measures to the replacement of energy supply systems and combinations therewithin. The classical decision theories of Wald, Savage, and Hurwicz are used to perform non-probabilistic optimisation under both technical and economic uncertainty. Such techniques, though simple in their handling of uncertainty, may elucidate robust decisions when the use of more intensive, probabilistic assessments of uncertainty is either infeasible or impractical.},\n  keywords = {Building retrofits,Economic optimisation,Robust decision-making,Uncertainty,Whole-building simulation}\n}\n\n
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\n In a prior study, the authors showed that decomposing holistic, blackbox building energy models into discrete components can increase the computational efficiency of large-scale retrofit analysis. This paper presents an extension of that methodology to include an economic cost-benefit model. The entire framework now comprises an integrated modelling procedure for the simulation and optimisation of retrofit decisions for individual buildings. Potential decisions can range from the installation of demand-side measures to the replacement of energy supply systems and combinations therewithin. The classical decision theories of Wald, Savage, and Hurwicz are used to perform non-probabilistic optimisation under both technical and economic uncertainty. Such techniques, though simple in their handling of uncertainty, may elucidate robust decisions when the use of more intensive, probabilistic assessments of uncertainty is either infeasible or impractical.\n
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\n \n\n \n \n \n \n \n \n A Bottom-up Energy Analysis across a Diverse Urban Building Portfolio: Retrofits for the Buildings at the Royal Botanic Gardens, Kew, UK.\n \n \n \n \n\n\n \n Ward, R. M., & Choudhary, R.\n\n\n \n\n\n\n Building and Environment. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{ward_bottom-up_2013,\n  title = {A {{Bottom-up Energy Analysis}} across a {{Diverse Urban Building Portfolio}}: {{Retrofits}} for the {{Buildings}} at the {{Royal Botanic Gardens}}, {{Kew}}, {{UK}}},\n  shorttitle = {A {{Bottom-up Energy Analysis}} across a {{Diverse Urban Building Portfolio}}},\n  author = {Ward, Rebecca Mary and Choudhary, Ruchi},\n  year = {2013},\n  journal = {Building and Environment},\n  issn = {0360-1323},\n  doi = {10.1016/j.buildenv.2013.12.018},\n  url = {http://www.sciencedirect.com/science/article/pii/S0360132313003740},\n  urldate = {2014-01-10TZ},\n  abstract = {A methodology for the analysis of building energy retrofits has been developed for a diverse set of buildings at the Royal Botanic Gardens (RBG), Kew in southwest London, UK. The methodology requires selection of appropriate building simulation tools dependent on the nature of the principal energy demand. This has involved the development of a stand-alone model to simulate the heat flow in botanical glasshouses, as well as stochastic simulation of electricity demand for buildings with high equipment density and occupancy-led operation. Application of the methodology to the buildings at RBG Kew illustrates the potential reduction in energy consumption at the building scale achievable from the application of retrofit measures deemed appropriate for heritage buildings and the potential benefit to be gained from onsite generation and supply of energy.},\n  project = {GES},\n  keywords = {district energy optimization,greenhouse simulation,retrofit analysis of buildings,stochastic electricity demand}\n}\n\n
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\n A methodology for the analysis of building energy retrofits has been developed for a diverse set of buildings at the Royal Botanic Gardens (RBG), Kew in southwest London, UK. The methodology requires selection of appropriate building simulation tools dependent on the nature of the principal energy demand. This has involved the development of a stand-alone model to simulate the heat flow in botanical glasshouses, as well as stochastic simulation of electricity demand for buildings with high equipment density and occupancy-led operation. Application of the methodology to the buildings at RBG Kew illustrates the potential reduction in energy consumption at the building scale achievable from the application of retrofit measures deemed appropriate for heritage buildings and the potential benefit to be gained from onsite generation and supply of energy.\n
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\n \n\n \n \n \n \n \n \n Distributed Energy Resource System Optimisation Using Mixed Integer Linear Programming.\n \n \n \n \n\n\n \n Omu, A., Choudhary, R., & Boies, A.\n\n\n \n\n\n\n Energy Policy, 61: 249–266. October 2013.\n \n\n\n\n
\n\n\n\n \n \n \"DistributedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{omu_distributed_2013,\n  title = {Distributed Energy Resource System Optimisation Using Mixed Integer Linear Programming},\n  author = {Omu, A. and Choudhary, R. and Boies, A.},\n  year = {2013},\n  month = oct,\n  journal = {Energy Policy},\n  volume = {61},\n  pages = {249--266},\n  issn = {0301-4215},\n  doi = {10.1016/j.enpol.2013.05.009},\n  url = {http://www.sciencedirect.com/science/article/pii/S0301421513003418},\n  urldate = {2014-01-10TZ},\n  abstract = {In this study a mixed integer linear programming (MILP) model is created for the design (i.e. technology selection, unit sizing, unit location, and distribution network structure) of a distributed energy system that meets the electricity and heating demands of a cluster of commercial and residential buildings while minimising annual investment and operating cost. The model is used to analyse the economic and environmental impacts of distributed energy systems at the neighbourhood scale in comparison to conventional centralised energy generation systems. Additionally, the influence of energy subsidies, such as the UK's Renewable Heat Incentives and Feed in Tariffs, is analysed to determine if they have the desired effect of increasing the economic competitiveness of renewable energy systems.},\n  project = {district\\_energy},\n  keywords = {Distributed energy resources,Energy subsidies,MILP}\n}\n\n
\n
\n\n\n
\n In this study a mixed integer linear programming (MILP) model is created for the design (i.e. technology selection, unit sizing, unit location, and distribution network structure) of a distributed energy system that meets the electricity and heating demands of a cluster of commercial and residential buildings while minimising annual investment and operating cost. The model is used to analyse the economic and environmental impacts of distributed energy systems at the neighbourhood scale in comparison to conventional centralised energy generation systems. Additionally, the influence of energy subsidies, such as the UK's Renewable Heat Incentives and Feed in Tariffs, is analysed to determine if they have the desired effect of increasing the economic competitiveness of renewable energy systems.\n
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\n \n\n \n \n \n \n \n \n Risk Based Lifetime Costs Assessment of a Ground Source Heat Pump (GSHP) System Design: Methodology and Case Study.\n \n \n \n \n\n\n \n Garber, D., Choudhary, R., & Soga, K.\n\n\n \n\n\n\n Building and Environment, 60: 66–80. February 2013.\n \n\n\n\n
\n\n\n\n \n \n \"RiskPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{garber_risk_2013,\n  title = {Risk Based Lifetime Costs Assessment of a Ground Source Heat Pump ({{GSHP}}) System Design: {{Methodology}} and Case Study},\n  shorttitle = {Risk Based Lifetime Costs Assessment of a Ground Source Heat Pump ({{GSHP}}) System Design},\n  author = {Garber, D. and Choudhary, R. and Soga, K.},\n  year = {2013},\n  month = feb,\n  journal = {Building and Environment},\n  volume = {60},\n  pages = {66--80},\n  issn = {0360-1323},\n  doi = {10.1016/j.buildenv.2012.11.011},\n  url = {http://www.sciencedirect.com/science/article/pii/S0360132312003095},\n  urldate = {2014-01-10TZ},\n  abstract = {Space heating accounts for a large portion of the world's carbon dioxide emissions. Ground Source Heat Pumps (GSHPs) are a technology which can reduce carbon emissions from heating and cooling. GSHP system performance is however highly sensitive to deviation from design values of the actual annual energy extraction/rejection rates from/to the ground. In order to prevent failure and/or performance deterioration of GSHP systems it is possible to incorporate a safety factor in the design of the GSHP by over-sizing the ground heat exchanger (GHE). A methodology to evaluate the financial risk involved in over-sizing the GHE is proposed is this paper. A probability based approach is used to evaluate the economic feasibility of a hypothetical full-size GSHP system as compared to four alternative Heating Ventilation and Air Conditioning (HVAC) system configurations. The model of the GSHP system is developed in the TRNSYS energy simulation platform and calibrated with data from an actual hybrid GSHP system installed in the Department of Earth Science, University of Oxford, UK. Results of the analysis show that potential savings from a full-size GSHP system largely depend on projected HVAC system efficiencies and gas and electricity prices. Results of the risk analysis also suggest that a full-size GSHP with auxiliary back up is potentially the most economical system configuration.},\n  keywords = {Carbon savings,Ground source heat pump (GSHP),Heating ventilation and air conditioning (HVAC),Risk based design,Safety factor,Space heating and cooling}\n}\n\n
\n
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\n Space heating accounts for a large portion of the world's carbon dioxide emissions. Ground Source Heat Pumps (GSHPs) are a technology which can reduce carbon emissions from heating and cooling. GSHP system performance is however highly sensitive to deviation from design values of the actual annual energy extraction/rejection rates from/to the ground. In order to prevent failure and/or performance deterioration of GSHP systems it is possible to incorporate a safety factor in the design of the GSHP by over-sizing the ground heat exchanger (GHE). A methodology to evaluate the financial risk involved in over-sizing the GHE is proposed is this paper. A probability based approach is used to evaluate the economic feasibility of a hypothetical full-size GSHP system as compared to four alternative Heating Ventilation and Air Conditioning (HVAC) system configurations. The model of the GSHP system is developed in the TRNSYS energy simulation platform and calibrated with data from an actual hybrid GSHP system installed in the Department of Earth Science, University of Oxford, UK. Results of the analysis show that potential savings from a full-size GSHP system largely depend on projected HVAC system efficiencies and gas and electricity prices. Results of the risk analysis also suggest that a full-size GSHP with auxiliary back up is potentially the most economical system configuration.\n
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\n \n\n \n \n \n \n \n The Impact of District Heating Network Adoption on Achieving Zero Carbon Targets.\n \n \n \n\n\n \n Omu, A., Choudhary, R., & Young, A.\n\n\n \n\n\n\n In Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pages 112–119, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{omu2013impact,\n  title = {The Impact of District Heating Network Adoption on Achieving Zero Carbon Targets},\n  booktitle = {Proceedings of {{BS}} 2013: 13th Conference of the International Building Performance Simulation Association},\n  author = {Omu, Akomeno and Choudhary, Ruchi and Young, Alasdair},\n  year = {2013},\n  pages = {112--119}\n}\n\n
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\n \n\n \n \n \n \n \n Using Building Simulation to Create Marginal Abatement Cost Curve of Individual Buildings.\n \n \n \n\n\n \n Rysanek, A., & Choudhary, R.\n\n\n \n\n\n\n In Proceedings of the Conference of International Building Performance Simulation Association, Chambery, France, volume 2628, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{rysanek2013using,\n  title = {Using Building Simulation to Create Marginal Abatement Cost Curve of Individual Buildings},\n  booktitle = {Proceedings of the Conference of International Building Performance Simulation Association, Chambery, France},\n  author = {Rysanek, Adam and Choudhary, Ruchi},\n  year = {2013},\n  volume = {2628}\n}\n\n
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\n  \n 2012\n \n \n (12)\n \n \n
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\n \n\n \n \n \n \n \n Calibrating Micro-Level Models with Macro-Level Data Using Bayesian Regression Analysis.\n \n \n \n\n\n \n Booth, A., Choudhary, R., & Initiative, E. E. C.\n\n\n \n\n\n\n In Proceedings of the 12th IBPSA Conference, November 14–16, pages 641–648. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{booth2012calibrating,\n  title = {Calibrating Micro-Level Models with Macro-Level Data Using Bayesian Regression Analysis},\n  booktitle = {Proceedings of the 12th {{IBPSA}} Conference, November 14--16},\n  author = {Booth, Adam and Choudhary, Ruchi and Initiative, Energy Efficient Cities},\n  year = {2012},\n  pages = {641--648},\n  abstract = {Bottom-up engineering-based housing stock models play a useful role in assessing the impact of retrofits for residential buildings. Such models require calibrating, using micro-level energy measurements, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level. This paper outlines a method for using macro-level data to calibrate micro-level models. A combination of regression analysis and Bayesian inference is pro- posed. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types, whilst quantifying uncertainty in the empirical energy data and the generated energy estimates.}\n}\n\n
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\n Bottom-up engineering-based housing stock models play a useful role in assessing the impact of retrofits for residential buildings. Such models require calibrating, using micro-level energy measurements, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level. This paper outlines a method for using macro-level data to calibrate micro-level models. A combination of regression analysis and Bayesian inference is pro- posed. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types, whilst quantifying uncertainty in the empirical energy data and the generated energy estimates.\n
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\n \n\n \n \n \n \n \n Scalable Methodology for Energy Efficiency Retrofit Decision Analysis.\n \n \n \n\n\n \n Heo, Y., Zhao, F., Lee, S. H., Sun, Y., Kim, J., Augenbroe, G., & Muehleisen, e.\n\n\n \n\n\n\n In Fifth National Conference of IBPSA-USA, pages 513–520, 2012. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{heo2012scalable,\n  title = {Scalable Methodology for Energy Efficiency Retrofit Decision Analysis},\n  booktitle = {Fifth National Conference of {{IBPSA-USA}}},\n  author = {Heo, Yeonsook and Zhao, Fei and Lee, Sang Hoon and Sun, Yuming and Kim, Jinsol and Augenbroe, Godfried and Muehleisen, {\\relax RT}},\n  year = {2012},\n  pages = {513--520},\n  abstract = {This paper introduces a scalable methodology that supports energy retrofit decision-making at two levels. The methodology is based on normative energy models to provide objective and transparent benchmarking and assessment. The aggregate-level analysis evaluates the effectiveness of policy and business plans on energy savings by benchmarking the energy performance of a collection of buildings and projecting the effects of different retrofit scenarios over time. The individual- level analysis supports risk-conscious decision-making for building stakeholders by providing explicit information about the energy performance risks associated with specific retrofit alternatives. This paper describes model results for a small set of commercial buildings in the Chicago Loop and findings relevant to the method{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}s application.}\n}\n\n
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\n This paper introduces a scalable methodology that supports energy retrofit decision-making at two levels. The methodology is based on normative energy models to provide objective and transparent benchmarking and assessment. The aggregate-level analysis evaluates the effectiveness of policy and business plans on energy savings by benchmarking the energy performance of a collection of buildings and projecting the effects of different retrofit scenarios over time. The individual- level analysis supports risk-conscious decision-making for building stakeholders by providing explicit information about the energy performance risks associated with specific retrofit alternatives. This paper describes model results for a small set of commercial buildings in the Chicago Loop and findings relevant to the methodï¿$\\frac{1}{2}$s application.\n
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\n \n\n \n \n \n \n \n Algorithmic and Declarative Modelling of a Greenhouse.\n \n \n \n\n\n \n Brown, J., Ward, R. M., Choudhary, R., & Slater, T.\n\n\n \n\n\n\n In 5th International Building Physics Conference, 2012. Kyoto University\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{brown2012algorithmic,\n  title = {Algorithmic and Declarative Modelling of a Greenhouse},\n  booktitle = {5th International Building Physics Conference},\n  author = {Brown, Jason and Ward, Rebecca Mary and Choudhary, Ruchi and Slater, Tom},\n  year = {2012},\n  publisher = {Kyoto University},\n  abstract = {Greenhouses contain physical processes of heat and mass transfer that are not accounted for in typical building energy simulation programs, especially those involving the interaction between plants and the indoor environment. This includes convective, radiative, as well as latent heat exchange. The primary aim of this paper is to investigate modelling strategies for analyzing the historic greenhouses at the Kew Botanical Gardens in London for the purpose of evaluating appropriate retrofits for energy savings. Two modelling and simulation paradigms are explored in this paper: A general greenhouse model, adapted from the Gembloux Dynamic Greenhouse Climate Model (GDGCM) of Pieters and Deltour, is implemented in two independent platforms, one being algorithmic MATLAB code and the other being the acausal declarative modelling language Modelica. The paper describes the key differences between the two modelling paradigms in terms of following features: (a) adequate elucidation of the physical behaviour, (b) numerical behaviour of the model in terms of stability, (c) flexibility for model improvements and extensions, (d) relevance to retrofit objectives. Results from the two models are compared to measured data from the particular greenhouse. These comparisons help understand the relative accuracy of the two models and are also indicative of areas where model resolution needs improvements.}\n}\n\n
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\n Greenhouses contain physical processes of heat and mass transfer that are not accounted for in typical building energy simulation programs, especially those involving the interaction between plants and the indoor environment. This includes convective, radiative, as well as latent heat exchange. The primary aim of this paper is to investigate modelling strategies for analyzing the historic greenhouses at the Kew Botanical Gardens in London for the purpose of evaluating appropriate retrofits for energy savings. Two modelling and simulation paradigms are explored in this paper: A general greenhouse model, adapted from the Gembloux Dynamic Greenhouse Climate Model (GDGCM) of Pieters and Deltour, is implemented in two independent platforms, one being algorithmic MATLAB code and the other being the acausal declarative modelling language Modelica. The paper describes the key differences between the two modelling paradigms in terms of following features: (a) adequate elucidation of the physical behaviour, (b) numerical behaviour of the model in terms of stability, (c) flexibility for model improvements and extensions, (d) relevance to retrofit objectives. Results from the two models are compared to measured data from the particular greenhouse. These comparisons help understand the relative accuracy of the two models and are also indicative of areas where model resolution needs improvements.\n
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\n \n\n \n \n \n \n \n \n Gaussian Process Modeling for Measurement and Verification of Building Energy Savings.\n \n \n \n \n\n\n \n Heo, Y., & Zavala, V. M.\n\n\n \n\n\n\n Energy and Buildings, 53: 7–18. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"GaussianPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{Heo20127,\n  title = {Gaussian Process Modeling for Measurement and Verification of Building Energy Savings},\n  author = {Heo, Yeonsook and Zavala, Victor M.},\n  year = {2012},\n  journal = {Energy and Buildings},\n  volume = {53},\n  pages = {7--18},\n  issn = {0378-7788},\n  doi = {http://dx.doi.org/10.1016/j.enbuild.2012.06.024},\n  url = {http://www.sciencedirect.com/science/article/pii/S037877881200312X},\n  abstract = {We present a Gaussian process (GP) modeling framework to determine energy savings and uncertainty levels in measurement and verification (M\\&amp;V) practices. Existing M\\&amp;V guidelines provide savings calculation procedures based on linear regression techniques that are limited in their predictive and uncertainty estimation capabilities. We demonstrate that, unlike linear regression, \\{GP\\} models can capture complex nonlinear and multivariable interactions as well as multiresolution trends of energy behavior. In addition, because \\{GP\\} models are developed under a Bayesian setting, they can capture different sources of uncertainty in a more systematic way. We demonstrate that these capabilities can ultimately lead to significantly less expensive M\\&amp;V practices. We illustrate the developments using simulated and real data settings.},\n  keywords = {Gaussian process modeling,Measurement and verification,Performance-based contracts,Retrofit analysis,Uncertainty}\n}\n\n
\n
\n\n\n
\n We present a Gaussian process (GP) modeling framework to determine energy savings and uncertainty levels in measurement and verification (M&V) practices. Existing M&V guidelines provide savings calculation procedures based on linear regression techniques that are limited in their predictive and uncertainty estimation capabilities. We demonstrate that, unlike linear regression, \\GP\\ models can capture complex nonlinear and multivariable interactions as well as multiresolution trends of energy behavior. In addition, because \\GP\\ models are developed under a Bayesian setting, they can capture different sources of uncertainty in a more systematic way. We demonstrate that these capabilities can ultimately lead to significantly less expensive M&V practices. We illustrate the developments using simulated and real data settings.\n
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\n \n\n \n \n \n \n \n The Use of Adaptive Zoning for Modelling Choice of Travel Modes.\n \n \n \n\n\n \n Hagen-Zanker, A, & Jin, Y\n\n\n \n\n\n\n Transaction in GIS, 17: 706–723. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{hagen-zanker_use_2012,\n  title = {The Use of Adaptive Zoning for Modelling Choice of Travel Modes},\n  author = {{Hagen-Zanker}, A and Jin, Y},\n  year = {2012},\n  journal = {Transaction in GIS},\n  volume = {17},\n  pages = {706--723},\n  doi = {10.1111/j.1467-9671.2012.01372.x},\n  abstract = {The use of adaptive zoning for modelling choice of travel modes}\n}\n\n
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\n\n\n
\n The use of adaptive zoning for modelling choice of travel modes\n
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\n \n\n \n \n \n \n \n \n A Probabilistic Energy Model for Non-Domestic Building Sectors Applied to Analysis of School Buildings in Greater London.\n \n \n \n \n\n\n \n Tian, W., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 54: 1–11. November 2012.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{tian_probabilistic_2012,\n  title = {A Probabilistic Energy Model for Non-Domestic Building Sectors Applied to Analysis of School Buildings in Greater {{London}}},\n  author = {Tian, W. and Choudhary, R.},\n  year = {2012},\n  month = nov,\n  journal = {Energy and Buildings},\n  volume = {54},\n  pages = {1--11},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2012.06.031},\n  url = {http://www.sciencedirect.com/science/article/pii/S0378778812003544},\n  urldate = {2014-01-10TZ},\n  abstract = {The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method.},\n  keywords = {Building stock,Energy simulation,Inverse problem,Probabilistic analysis,Sensitivity analysis}\n}\n\n
\n
\n\n\n
\n The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method.\n
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\n \n\n \n \n \n \n \n \n A Decoupled Whole-Building Simulation Engine for Rapid Exhaustive Search of Low-Carbon and Low-Energy Building Refurbishment Options.\n \n \n \n \n\n\n \n Rysanek, A. M., & Choudhary, R.\n\n\n \n\n\n\n Building and Environment, 50: 21–33. April 2012.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{rysanek_decoupled_2012,\n  title = {A Decoupled Whole-Building Simulation Engine for Rapid Exhaustive Search of Low-Carbon and Low-Energy Building Refurbishment Options},\n  author = {Rysanek, A. M. and Choudhary, R.},\n  year = {2012},\n  month = apr,\n  journal = {Building and Environment},\n  volume = {50},\n  pages = {21--33},\n  issn = {0360-1323},\n  doi = {10.1016/j.buildenv.2011.09.024},\n  url = {http://www.sciencedirect.com/science/article/pii/S0360132311003039},\n  urldate = {2014-01-10TZ},\n  abstract = {This paper presents the development of a new building physics and energy supply systems simulation platform. It has been adapted from both existing commercial models and empirical works, but designed to provide expedient exhaustive simulation of all salient types of energy- and carbon-reducing retrofit options. These options may include any combination of behavioural measures, building fabric and equipment upgrades, improved HVAC control strategies, or novel low-carbon energy supply technologies. We provide a methodological description of the proposed model, followed by two illustrative case studies of the tool when used to investigate retrofit options of a mixed-use office building and primary school in the UK. It is not the intention of this paper, nor would it be feasible, to provide a complete engineering decomposition of the proposed model, describing all calculation processes in detail. Instead, this paper concentrates on presenting the particular engineering aspects of the model which steer away from conventional practise.},\n  keywords = {Building retrofits,Discrete options analysis,Energy efficiency emissions reduction,Whole-building simulation}\n}\n\n
\n
\n\n\n
\n This paper presents the development of a new building physics and energy supply systems simulation platform. It has been adapted from both existing commercial models and empirical works, but designed to provide expedient exhaustive simulation of all salient types of energy- and carbon-reducing retrofit options. These options may include any combination of behavioural measures, building fabric and equipment upgrades, improved HVAC control strategies, or novel low-carbon energy supply technologies. We provide a methodological description of the proposed model, followed by two illustrative case studies of the tool when used to investigate retrofit options of a mixed-use office building and primary school in the UK. It is not the intention of this paper, nor would it be feasible, to provide a complete engineering decomposition of the proposed model, describing all calculation processes in detail. Instead, this paper concentrates on presenting the particular engineering aspects of the model which steer away from conventional practise.\n
\n\n\n
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\n \n\n \n \n \n \n \n \n Calibration of Building Energy Models for Retrofit Analysis under Uncertainty.\n \n \n \n \n\n\n \n Heo, Y., Choudhary, R., & Augenbroe, G. A.\n\n\n \n\n\n\n Energy and Buildings, 47: 550–560. April 2012.\n \n\n\n\n
\n\n\n\n \n \n \"CalibrationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{heo_calibration_2012,\n  title = {Calibration of Building Energy Models for Retrofit Analysis under Uncertainty},\n  author = {Heo, Y. and Choudhary, R. and Augenbroe, G. A.},\n  year = {2012},\n  month = apr,\n  journal = {Energy and Buildings},\n  volume = {47},\n  pages = {550--560},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2011.12.029},\n  url = {http://www.sciencedirect.com/science/article/pii/S037877881100644X},\n  urldate = {2014-01-10TZ},\n  abstract = {Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.},\n  keywords = {Bayesian calibration,Normative energy models,Retrofit analysis,Uncertainty analysis}\n}\n\n
\n
\n\n\n
\n Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.\n
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\n \n\n \n \n \n \n \n \n A New Method of Adaptive Zoning for Spatial Interaction Models.\n \n \n \n \n\n\n \n Hagen-Zanker, A., & Jin, Y.\n\n\n \n\n\n\n Geographical Analysis, 44(4): 281–301. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{hagen-zanker_new_2012,\n  title = {A {{New Method}} of {{Adaptive Zoning}} for {{Spatial Interaction Models}}},\n  author = {{Hagen-Zanker}, A. and Jin, Y.},\n  year = {2012},\n  journal = {Geographical Analysis},\n  volume = {44},\n  number = {4},\n  pages = {281--301},\n  issn = {1538-4632},\n  doi = {10.1111/j.1538-4632.2012.00855.x},\n  url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2012.00855.x/abstract},\n  urldate = {2014-01-10TZ},\n  abstract = {Spatial interaction models commonly use discrete zones to represent locations. The computational requirements of the models normally arise with the square of the number of zones or worse. For computationally intensive models, such as land use{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}transport interaction models and activity-based models for city regions, this dependency of zone size is a long-standing problem that has not disappeared even with increasing computation speed in PCs{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}it still forces modelers to compromise on the spatial resolution and extent of model coverage as well as on the rigor and depth of model-based analysis. This article introduces a new type of discrete zone system, with the objective of reducing the time for estimating and applying spatial interaction models while maintaining their accuracy. The premise of the new system is that the appropriate size of destination zones depends on the distance to their origin zone: at short distances, spatial accuracy is important and destination zones must be small; at longer distances, knowing the precise location becomes less important and zones can be larger. The new method defines a specific zone map for every origin zone; each origin zone becomes the focus of its own map, surrounded by small zones nearby and large zones farther away. We present the theoretical formulation of the new method and test it with a model of commuting in England. The results of the new method are equivalent to those of the conventional model, despite reducing the number of zone pairs by 96\\% and the computation time by 70\\%.},\n  copyright = {{\\"i}{\\textquestiondown}{$\\frac{1}{2}$} 2012 The Ohio State University},\n  langid = {english}\n}\n\n
\n
\n\n\n
\n Spatial interaction models commonly use discrete zones to represent locations. The computational requirements of the models normally arise with the square of the number of zones or worse. For computationally intensive models, such as land useï¿$\\frac{1}{2}$transport interaction models and activity-based models for city regions, this dependency of zone size is a long-standing problem that has not disappeared even with increasing computation speed in PCsï¿$\\frac{1}{2}$it still forces modelers to compromise on the spatial resolution and extent of model coverage as well as on the rigor and depth of model-based analysis. This article introduces a new type of discrete zone system, with the objective of reducing the time for estimating and applying spatial interaction models while maintaining their accuracy. The premise of the new system is that the appropriate size of destination zones depends on the distance to their origin zone: at short distances, spatial accuracy is important and destination zones must be small; at longer distances, knowing the precise location becomes less important and zones can be larger. The new method defines a specific zone map for every origin zone; each origin zone becomes the focus of its own map, surrounded by small zones nearby and large zones farther away. We present the theoretical formulation of the new method and test it with a model of commuting in England. The results of the new method are equivalent to those of the conventional model, despite reducing the number of zone pairs by 96% and the computation time by 70%.\n
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\n \n\n \n \n \n \n \n \n Energy Analysis of the Non-Domestic Building Stock of Greater London.\n \n \n \n \n\n\n \n Choudhary, R.\n\n\n \n\n\n\n Building and Environment, 51: 243–254. May 2012.\n \n\n\n\n
\n\n\n\n \n \n \"EnergyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{choudhary_energy_2012,\n  title = {Energy Analysis of the Non-Domestic Building Stock of {{Greater London}}},\n  author = {Choudhary, R.},\n  year = {2012},\n  month = may,\n  journal = {Building and Environment},\n  volume = {51},\n  pages = {243--254},\n  issn = {0360-1323},\n  doi = {10.1016/j.buildenv.2011.10.006},\n  url = {http://www.sciencedirect.com/science/article/pii/S0360132311003556},\n  urldate = {2014-01-10TZ},\n  abstract = {This paper presents a Bayesian approach for developing city-scale energy models of the built environment and demonstrates its application to non-domestic buildings in Greater London. The work draws upon available information of the building stock, such as: mapping databases, floorspace statistics, energy benchmarks, and measured energy consumption reported in display energy certificates of public buildings. The resulting model is able to describe the spread due to variation of energy consumption across buildings within a similar category. These spreads (or distributions) can be used for estimating the probability distribution of the gross energy consumption per local authority in Greater London. The work is driven by the need to quantify future energy demand of buildings in their urban context as a function of projected growth of buildings and populations, refurbishments, policies incentivizing energy efficiency measures, and changes in building operation. The focus on the non-domestic sector enables a framework that accommodates diverse set of activities and uses of buildings within an urban region.},\n  keywords = {Bayesian Regression,Non-Domestic Buildings,Uncertainty Quantification,Urban-Scale Energy Consumption}\n}\n\n
\n
\n\n\n
\n This paper presents a Bayesian approach for developing city-scale energy models of the built environment and demonstrates its application to non-domestic buildings in Greater London. The work draws upon available information of the building stock, such as: mapping databases, floorspace statistics, energy benchmarks, and measured energy consumption reported in display energy certificates of public buildings. The resulting model is able to describe the spread due to variation of energy consumption across buildings within a similar category. These spreads (or distributions) can be used for estimating the probability distribution of the gross energy consumption per local authority in Greater London. The work is driven by the need to quantify future energy demand of buildings in their urban context as a function of projected growth of buildings and populations, refurbishments, policies incentivizing energy efficiency measures, and changes in building operation. The focus on the non-domestic sector enables a framework that accommodates diverse set of activities and uses of buildings within an urban region.\n
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\n \n\n \n \n \n \n \n \n Handling Uncertainty in Housing Stock Models.\n \n \n \n \n\n\n \n Booth, A. T., Choudhary, R., & Spiegelhalter, D. J.\n\n\n \n\n\n\n Building and Environment, 48: 35–47. February 2012.\n \n\n\n\n
\n\n\n\n \n \n \"HandlingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{booth_handling_2012,\n  title = {Handling Uncertainty in Housing Stock Models},\n  author = {Booth, A. T. and Choudhary, R. and Spiegelhalter, D. J.},\n  year = {2012},\n  month = feb,\n  journal = {Building and Environment},\n  volume = {48},\n  pages = {35--47},\n  issn = {0360-1323},\n  doi = {10.1016/j.buildenv.2011.08.016},\n  url = {http://www.sciencedirect.com/science/article/pii/S0360132311002599},\n  urldate = {2014-01-10TZ},\n  abstract = {Housing stock models can be useful tools in helping to assess the environmental and socio-economic impacts of retrofits to residential buildings; however, existing housing stock models are not able to quantify the uncertainties that arise in the modelling process from various sources, thus limiting the role that they can play in helping decision makers. This paper examines the different sources of uncertainty involved in housing stock models and proposes a framework for handling these uncertainties. This framework involves integrating probabilistic sensitivity analysis with a Bayesian calibration process in order to quantify uncertain parameters more accurately. The proposed framework is tested on a case study building, and suggestions are made on how to expand the framework for retrofit analysis at an urban-scale.},\n  keywords = {Bayesian,Calibration,Energy,Housing,Stock,Uncertainty}\n}\n\n
\n
\n\n\n
\n Housing stock models can be useful tools in helping to assess the environmental and socio-economic impacts of retrofits to residential buildings; however, existing housing stock models are not able to quantify the uncertainties that arise in the modelling process from various sources, thus limiting the role that they can play in helping decision makers. This paper examines the different sources of uncertainty involved in housing stock models and proposes a framework for handling these uncertainties. This framework involves integrating probabilistic sensitivity analysis with a Bayesian calibration process in order to quantify uncertain parameters more accurately. The proposed framework is tested on a case study building, and suggestions are made on how to expand the framework for retrofit analysis at an urban-scale.\n
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\n \n\n \n \n \n \n \n Displaying Uncertainty in Energy Savings for Large-Scale Building Refurbishments.\n \n \n \n\n\n \n Booth, A., & Choudhary, R.\n\n\n \n\n\n\n . 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{booth2012displaying,\n  title = {Displaying Uncertainty in Energy Savings for Large-Scale Building Refurbishments},\n  author = {Booth, Adam and Choudhary, Ruchi},\n  year = {2012}\n}\n\n
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\n  \n 2011\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n A Probabilistic Model for Assessing Energy Consumption of the Non-Domestic Building Stock.\n \n \n \n\n\n \n Choudhary, R., & Initiative, E. E. C.\n\n\n \n\n\n\n proc. of: Building Simulation. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{choudhary2011probabilistic,\n  title = {A Probabilistic Model for Assessing Energy Consumption of the Non-Domestic Building Stock},\n  author = {Choudhary, Ruchi and Initiative, Energy Efficient Cities},\n  year = {2011},\n  journal = {proc. of: Building Simulation},\n  abstract = {This paper presents a probabilistic framework for developing energy models of the built environment in a city and demonstrates its first-phase application to non-domestic buildings in Greater London. The work is driven by the need to quantify future energy demand of buildings in their urban context as a function of projected growth of buildings and populations, refurbishments, policies incentivizing energy efficiency measures, and changes in building operation. The focus on the non-domestic sector enables exploring a frame- work that accommodates diverse set of activities and uses of buildings within an urban region.}\n}\n\n
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\n This paper presents a probabilistic framework for developing energy models of the built environment in a city and demonstrates its first-phase application to non-domestic buildings in Greater London. The work is driven by the need to quantify future energy demand of buildings in their urban context as a function of projected growth of buildings and populations, refurbishments, policies incentivizing energy efficiency measures, and changes in building operation. The focus on the non-domestic sector enables exploring a frame- work that accommodates diverse set of activities and uses of buildings within an urban region.\n
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\n \n\n \n \n \n \n \n Risk Analysis of Energy-Efficiency Projects Based on Bayesian Calibration of Building Energy Models.\n \n \n \n\n\n \n Heo, Y., Augenbroe, G., & Choudhary, R.\n\n\n \n\n\n\n Building simulation,2579–2586. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{heo2011risk,\n  title = {Risk Analysis of Energy-Efficiency Projects Based on {{Bayesian}} Calibration of Building Energy Models},\n  author = {Heo, Yeonsook and Augenbroe, Godfried and Choudhary, Ruchi},\n  year = {2011},\n  journal = {Building simulation},\n  pages = {2579--2586},\n  abstract = {This paper presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This paper demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the ESCO industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach to standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.}\n}\n\n
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\n This paper presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This paper demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the ESCO industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach to standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.\n
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\n \n\n \n \n \n \n \n Energy Use of Buildings at Urban Scale: A Case Study of London School Buildings.\n \n \n \n\n\n \n Tian, W., Choudhary, R., & Initiative, E. E. C.\n\n\n \n\n\n\n Proceedings of building simulation 2011: 12th conference of international building performance simulation association, Sydney, November,14–16. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{tian2011energy,\n  title = {Energy Use of Buildings at Urban Scale: A Case Study of {{London}} School Buildings},\n  author = {Tian, Wei and Choudhary, Ruchi and Initiative, Energy Efficient Cities},\n  year = {2011},\n  journal = {Proceedings of building simulation 2011: 12th conference of international building performance simulation association, Sydney, November},\n  pages = {14--16},\n  abstract = {The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.}\n}\n\n
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\n The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.\n
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\n \n\n \n \n \n \n \n \n Revisiting Kappa to Account for Change in the Accuracy Assessment of Land-Use Change Models.\n \n \n \n \n\n\n \n van Vliet , J., Bregt, A. K., & Hagen-Zanker, A.\n\n\n \n\n\n\n Ecological Modelling, 222(8): 1367–1375. April 2011.\n \n\n\n\n
\n\n\n\n \n \n \"RevisitingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{van_vliet_revisiting_2011,\n  title = {Revisiting {{Kappa}} to Account for Change in the Accuracy Assessment of Land-Use Change Models},\n  author = {{van Vliet}, J. and Bregt, A. K. and {Hagen-Zanker}, A.},\n  year = {2011},\n  month = apr,\n  journal = {Ecological Modelling},\n  volume = {222},\n  number = {8},\n  pages = {1367--1375},\n  issn = {0304-3800},\n  doi = {10.1016/j.ecolmodel.2011.01.017},\n  url = {http://www.sciencedirect.com/science/article/pii/S0304380011000494},\n  urldate = {2014-01-10TZ},\n  abstract = {Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces KSimulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and KSimulation are compared using the results of a land-use model. It is found that only KSimulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time.},\n  keywords = {Accuracy assessment,Kappa statistic,Land-use change,map comparison,Model calibration}\n}\n\n
\n
\n\n\n
\n Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces KSimulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and KSimulation are compared using the results of a land-use model. It is found that only KSimulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time.\n
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\n \n\n \n \n \n \n \n \n Air Quality and Public Health Impacts of UK Airports. Part I: Emissions.\n \n \n \n \n\n\n \n Stettler, M. E. J., Eastham, S., & Barrett, S. R. H.\n\n\n \n\n\n\n Atmospheric Environment, 45(31): 5415–5424. October 2011.\n \n\n\n\n
\n\n\n\n \n \n \"AirPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{stettler_air_2011,\n  title = {Air Quality and Public Health Impacts of {{UK}} Airports. {{Part I}}: {{Emissions}}},\n  shorttitle = {Air Quality and Public Health Impacts of {{UK}} Airports. {{Part I}}},\n  author = {Stettler, M. E. J. and Eastham, S. and Barrett, S. R. H.},\n  year = {2011},\n  month = oct,\n  journal = {Atmospheric Environment},\n  volume = {45},\n  number = {31},\n  pages = {5415--5424},\n  issn = {1352-2310},\n  doi = {10.1016/j.atmosenv.2011.07.012},\n  url = {http://www.sciencedirect.com/science/article/pii/S135223101100728X},\n  urldate = {2014-01-10TZ},\n  abstract = {The potential adverse human health and climate impacts of emissions from UK airports have become a significant political issue, yet the emissions, air quality impacts and health impacts attributable to UK airports remain largely unstudied. We produce an inventory of UK airport emissions {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} including aircraft landing and takeoff (LTO) operations and airside support equipment {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} with uncertainties quantified. The airports studied account for more than 95\\% of UK air passengers in 2005. We estimate that in 2005, UK airports emitted 10.2{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Gg [-23 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+29\\%] of NOx, 0.73{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Gg [-29 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+32\\%] of SO2, 11.7{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Gg [-42 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+77\\%] of CO, 1.8{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Gg [-59 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+155\\%] of HC, 2.4{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Tg [-13 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+12\\%] of CO2, and 0.31{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Gg [-36 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+45\\%] of PM2.5. This translates to 2.5{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}Tg [-12 to{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}+12\\%] CO2-eq using Global Warming Potentials for a 100-year time horizon. Uncertainty estimates were based on analysis of data from aircraft emissions measurement campaigns and analyses of aircraft operations. The First-Order Approximation (FOA3) {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} currently the standard approach used to estimate particulate matter emissions from aircraft {\\"i}{\\textquestiondown}{$\\frac{1}{2}$} is compared to measurements and it is shown that there are discrepancies greater than an order of magnitude for 40\\% of cases for both organic carbon and black carbon emissions indices. Modified methods to approximate organic carbon emissions, arising from incomplete combustion and lubrication oil, and black carbon are proposed. These alterations lead to factor 8 and a 44\\% increase in the annual emissions estimates of black and organic carbon particulate matter, respectively, leading to a factor 3.4 increase in total PM2.5 emissions compared to the current FOA3 methodology. Our{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}estimates of emissions are used in Part II to quantify the air quality and health impacts of UK airports, to assess mitigation options, and to estimate the impacts of a potential London airport expansion.},\n  keywords = {Air quality,Airport,Aviation,Emissions,Particulate matter}\n}\n\n
\n
\n\n\n
\n The potential adverse human health and climate impacts of emissions from UK airports have become a significant political issue, yet the emissions, air quality impacts and health impacts attributable to UK airports remain largely unstudied. We produce an inventory of UK airport emissions ï¿$\\frac{1}{2}$ including aircraft landing and takeoff (LTO) operations and airside support equipment ï¿$\\frac{1}{2}$ with uncertainties quantified. The airports studied account for more than 95% of UK air passengers in 2005. We estimate that in 2005, UK airports emitted 10.2ï¿$\\frac{1}{2}$Gg [-23 toï¿$\\frac{1}{2}$+29%] of NOx, 0.73ï¿$\\frac{1}{2}$Gg [-29 toï¿$\\frac{1}{2}$+32%] of SO2, 11.7ï¿$\\frac{1}{2}$Gg [-42 toï¿$\\frac{1}{2}$+77%] of CO, 1.8ï¿$\\frac{1}{2}$Gg [-59 toï¿$\\frac{1}{2}$+155%] of HC, 2.4ï¿$\\frac{1}{2}$Tg [-13 toï¿$\\frac{1}{2}$+12%] of CO2, and 0.31ï¿$\\frac{1}{2}$Gg [-36 toï¿$\\frac{1}{2}$+45%] of PM2.5. This translates to 2.5ï¿$\\frac{1}{2}$Tg [-12 toï¿$\\frac{1}{2}$+12%] CO2-eq using Global Warming Potentials for a 100-year time horizon. Uncertainty estimates were based on analysis of data from aircraft emissions measurement campaigns and analyses of aircraft operations. The First-Order Approximation (FOA3) ï¿$\\frac{1}{2}$ currently the standard approach used to estimate particulate matter emissions from aircraft ï¿$\\frac{1}{2}$ is compared to measurements and it is shown that there are discrepancies greater than an order of magnitude for 40% of cases for both organic carbon and black carbon emissions indices. Modified methods to approximate organic carbon emissions, arising from incomplete combustion and lubrication oil, and black carbon are proposed. These alterations lead to factor 8 and a 44% increase in the annual emissions estimates of black and organic carbon particulate matter, respectively, leading to a factor 3.4 increase in total PM2.5 emissions compared to the current FOA3 methodology. Ourï¿$\\frac{1}{2}$estimates of emissions are used in Part II to quantify the air quality and health impacts of UK airports, to assess mitigation options, and to estimate the impacts of a potential London airport expansion.\n
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\n \n\n \n \n \n \n \n \n A Framework of Map Comparison Methods to Evaluate Geosimulation Models from a Geospatial Perspective.\n \n \n \n \n\n\n \n Hagen-Zanker, A., & Martens, P.\n\n\n \n\n\n\n In Murgante, B., Borruso, G., & Lapucci, A., editor(s), Geocomputation, Sustainability and Environmental Planning, of Studies in Computational Intelligence, pages 251–269. Springer Berlin Heidelberg, January 2011.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@incollection{hagen-zanker_framework_2011,\n  title = {A {{Framework}} of {{Map Comparison Methods}} to {{Evaluate Geosimulation Models}} from a {{Geospatial Perspective}}},\n  booktitle = {Geocomputation, {{Sustainability}} and {{Environmental Planning}}},\n  author = {{Hagen-Zanker}, A. and Martens, P.},\n  editor = {Murgante, B. and Borruso, G. and Lapucci, A.},\n  year = {2011},\n  month = jan,\n  series = {Studies in {{Computational Intelligence}}},\n  number = {348},\n  pages = {251--269},\n  publisher = {Springer Berlin Heidelberg},\n  url = {http://link.springer.com/chapter/10.1007/978-3-642-19733-8_14},\n  urldate = {2014-01-10TZ},\n  abstract = {Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro-level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in model results. Such evaluation requires a geospatial perspective; it is not sufficient if micro-level interactions are realistic. Importantly, interactions should be such that meso- and macro- level patterns emerging from the model are realistic. In recent years, a host of map comparison methods have been developed, which address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions.},\n  copyright = {{\\"i}{\\textquestiondown}{$\\frac{1}{2}$}2011 Springer-Verlag Berlin Heidelberg},\n  isbn = {978-3-642-19732-1 978-3-642-19733-8},\n  keywords = {Appl.Mathematics/Computational Methods of Engineering,Artificial Intelligence (incl. Robotics),Calibration,Computer Applications in Earth Sciences,geosimulation,Landscape/Regional and Urban Planning,map comparison,Math. Appl. in Environmental Science,validation}\n}\n\n
\n
\n\n\n
\n Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro-level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in model results. Such evaluation requires a geospatial perspective; it is not sufficient if micro-level interactions are realistic. Importantly, interactions should be such that meso- and macro- level patterns emerging from the model are realistic. In recent years, a host of map comparison methods have been developed, which address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions.\n
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\n \n\n \n \n \n \n \n Multi-Period Optimization of Building Refurbishment Decisions: Assessing Options and Risk under Economic Uncertainty.\n \n \n \n\n\n \n Rysanek, A., & Choudhary, R.\n\n\n \n\n\n\n In Proceedings of Buildings Simulation, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{rysanek2011multi,\n  title = {Multi-Period Optimization of Building Refurbishment Decisions: {{Assessing}} Options and Risk under Economic Uncertainty},\n  booktitle = {Proceedings of Buildings Simulation},\n  author = {Rysanek, Adam and Choudhary, Ruchi},\n  year = {2011}\n}\n\n
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\n  \n 2010\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n A Predictive Model for Computing the Influence of Space Layouts on Nurses' Movement in Hospital Units.\n \n \n \n\n\n \n Choudhary, R., Bafna, S., Heo, Y., Hendrich, A., & Chow, M.\n\n\n \n\n\n\n Journal of Building Performance Simulation, 3(3): 171–184. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{choudhary2010predictive,\n  title = {A Predictive Model for Computing the Influence of Space Layouts on Nurses' Movement in Hospital Units},\n  author = {Choudhary, Ruchi and Bafna, Sonit and Heo, Yeonsook and Hendrich, Ann and Chow, Marilyn},\n  year = {2010},\n  journal = {Journal of Building Performance Simulation},\n  volume = {3},\n  number = {3},\n  pages = {171--184},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n  \n 2009\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Unit-Related Factors That Affect Nursing Time with Patients: Spatial Analysis of the Time and Motion Study.\n \n \n \n\n\n \n Hendrich, A., Chow, M. P, Bafna, S., Choudhary, R., Heo, Y., & Skierczynski, B. A\n\n\n \n\n\n\n HERD: Health Environments Research & Design Journal, 2(2): 5–20. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hendrich2009unit,\n  title = {Unit-Related Factors That Affect Nursing Time with Patients: {{Spatial}} Analysis of the Time and Motion Study},\n  author = {Hendrich, Ann and Chow, Marilyn P and Bafna, Sonit and Choudhary, Ruchi and Heo, Yeonsook and Skierczynski, Boguslaw A},\n  year = {2009},\n  journal = {HERD: Health Environments Research \\& Design Journal},\n  volume = {2},\n  number = {2},\n  pages = {5--20},\n  publisher = {SAGE Publications Sage CA: Los Angeles, CA}\n}\n\n
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\n \n\n \n \n \n \n \n A Modeling Approach for Estimating the Impact of Spatial Configuration on Nurses' Movement.\n \n \n \n\n\n \n Heo, Y., Choudhary, R., Bafna, S., Hendrich, A., & Chow, M. P\n\n\n \n\n\n\n In Proceedings of the 7th International Space Syntax Symposium, volume 1, pages 41, 2009. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{heo2009modeling,\n  title = {A Modeling Approach for Estimating the Impact of Spatial Configuration on Nurses' Movement},\n  booktitle = {Proceedings of the 7th International Space Syntax Symposium},\n  author = {Heo, Yeonsook and Choudhary, Ruchi and Bafna, Sonit and Hendrich, Ann and Chow, Marylyn P},\n  year = {2009},\n  volume = {1},\n  pages = {41}\n}\n\n
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\n \n\n \n \n \n \n \n Computational Fluid Dynamics in an Equation-Based Modeling Environment.\n \n \n \n\n\n \n Brown, J., Augenbroe, G., Choudhary, R., & Paredis, C.\n\n\n \n\n\n\n . 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{brown2009computational,\n  title = {Computational Fluid Dynamics in an Equation-Based Modeling Environment},\n  author = {Brown, Jason and Augenbroe, Godfried and Choudhary, Ruchi and Paredis, Christiaan},\n  year = {2009}\n}\n\n
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\n  \n 2008\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Simulation-Enhanced Prototyping of an Experimental Solar House.\n \n \n \n\n\n \n Choudhary, R., Augenbroe, G., Gentry, R., & Hu, H.\n\n\n \n\n\n\n In Building Simulation, volume 1, pages 336–355, 2008. Springer Berlin Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{choudhary2008simulation,\n  title = {Simulation-Enhanced Prototyping of an Experimental Solar House},\n  booktitle = {Building Simulation},\n  author = {Choudhary, Ruchi and Augenbroe, Godfried and Gentry, Russell and Hu, Huafen},\n  year = {2008},\n  volume = {1},\n  pages = {336--355},\n  publisher = {Springer Berlin Heidelberg}\n}\n\n
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\n  \n 2007\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n A Study of Variations among Mies's Courtyard Houses by a Combined Set of Visual and Environmental Properties.\n \n \n \n\n\n \n Choudhary, R., Heo, Y., & Bafna, S.\n\n\n \n\n\n\n In Proceedings of the Sixth International Space Syntax Symposium. İstanbul, Turkey, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{choudhary2007study,\n  title = {A Study of Variations among Mies's Courtyard Houses by a Combined Set of Visual and Environmental Properties},\n  booktitle = {Proceedings of the Sixth International Space Syntax Symposium. {{{\\.I}stanbul}}, Turkey},\n  author = {Choudhary, Ruchi and Heo, Yeonsook and Bafna, Sonit},\n  year = {2007}\n}\n\n
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\n \n\n \n \n \n \n \n Feasibility of Controlled Hybrid Ventilation in Mid Rise Apartments in the Usa.\n \n \n \n\n\n \n Hu, H., Augenbroe, G., & Choudhary, R.\n\n\n \n\n\n\n Proceedings: Building simulation 2007,475–485. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hu2007feasibility,\n  title = {Feasibility of Controlled Hybrid Ventilation in Mid Rise Apartments in the Usa},\n  author = {Hu, Huafen and Augenbroe, Godfried and Choudhary, Ruchi},\n  year = {2007},\n  journal = {Proceedings: Building simulation 2007},\n  pages = {475--485}\n}\n\n
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\n  \n 2005\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n Analytic Target Cascading in Simulation-Based Building Design.\n \n \n \n\n\n \n Choudhary, R, Malkawi, A, & Papalambros, e.\n\n\n \n\n\n\n Automation in construction, 14(4): 551–568. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{choudhary2005analytic,\n  title = {Analytic Target Cascading in Simulation-Based Building Design},\n  author = {Choudhary, R and Malkawi, A and Papalambros, {\\relax PY}},\n  year = {2005},\n  journal = {Automation in construction},\n  volume = {14},\n  number = {4},\n  pages = {551--568},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Decision Support and Design Evolution: Integrating Genetic Algorithms, CFD and Visualization.\n \n \n \n\n\n \n Malkawi, A. M, Srinivasan, R. S, Yun, K Y., & Choudhary, R.\n\n\n \n\n\n\n Automation in construction, 14(1): 33–44. 2005.\n \n\n\n\n
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@article{malkawi2005decision,\n  title = {Decision Support and Design Evolution: Integrating Genetic Algorithms, {{CFD}} and Visualization},\n  author = {Malkawi, Ali M and Srinivasan, Ravi S and Yun, K Yi and Choudhary, Ruchi},\n  year = {2005},\n  journal = {Automation in construction},\n  volume = {14},\n  number = {1},\n  pages = {33--44},\n  publisher = {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Design Optimization in Computer Aided Architectural Design.\n \n \n \n\n\n \n Choudhary, e., & Michalek, e.\n\n\n \n\n\n\n Proceedings of CAADRIA, The Association for Computer-Aided Architectural Design Research in Asia. New Delphi, India,149–158. 2005.\n \n\n\n\n
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@article{choudhary2005design,\n  title = {Design Optimization in Computer Aided Architectural Design},\n  author = {Choudhary, {\\relax RUCHI} and Michalek, {\\relax JEREMY}},\n  year = {2005},\n  journal = {Proceedings of CAADRIA, The Association for Computer-Aided Architectural Design Research in Asia. New Delphi, India},\n  pages = {149--158}\n}\n\n
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\n \n\n \n \n \n \n \n Simulation-Based Design by Hierarchical Optimization.\n \n \n \n\n\n \n Choudhary, R., Papalambros, e., & Malkawi, A. M\n\n\n \n\n\n\n In Proceedings of the Ninth International IBPSA Conference, 2005. \n \n\n\n\n
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@inproceedings{choudhary2005simulation,\n  title = {Simulation-Based Design by Hierarchical Optimization},\n  booktitle = {Proceedings of the Ninth International {{IBPSA}} Conference},\n  author = {Choudhary, Ruchi and Papalambros, {\\relax PY} and Malkawi, Ali M},\n  year = {2005}\n}\n\n
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\n \n\n \n \n \n \n \n A Hierarchical Design Optimization Approach for Meeting Building Performance Targets.\n \n \n \n\n\n \n Choudhary, R, Papalambros, e., & Malkawi, A\n\n\n \n\n\n\n Architectural Engineering and Design Management, 1(1): 57–76. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{choudhary2005hierarchical,\n  title = {A Hierarchical Design Optimization Approach for Meeting Building Performance Targets},\n  author = {Choudhary, R and Papalambros, {\\relax PY} and Malkawi, A},\n  year = {2005},\n  journal = {Architectural Engineering and Design Management},\n  volume = {1},\n  number = {1},\n  pages = {57--76},\n  publisher = {Taylor \\& Francis Group}\n}\n\n
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\n \n\n \n \n \n \n \n A Simplified Method to Estimate the Free Path Length Variance.\n \n \n \n\n\n \n Zhang, Y., Augenbroe, G., Choudhary, R., & Vidakovic, B.\n\n\n \n\n\n\n The Journal of the Acoustical Society of America, 117(4): 2580–2580. 2005.\n \n\n\n\n
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@article{zhang2005simplified,\n  title = {A Simplified Method to Estimate the Free Path Length Variance},\n  author = {Zhang, Yan and Augenbroe, Godfried and Choudhary, Ruchi and Vidakovic, Brani},\n  year = {2005},\n  journal = {The Journal of the Acoustical Society of America},\n  volume = {117},\n  number = {4},\n  pages = {2580--2580},\n  publisher = {Acoustical Society of America}\n}\n\n
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\n \n\n \n \n \n \n \n J. Won Choi and J. Eun Hwang KotaView: Simulating Traditional Korean Architecture Interactively and Intelligently on the Web................................. 1 AM Tanyer and G. Aouad Moving beyond the Fourth Dimension with an IFC-based Single Project Database.......................................... 15.\n \n \n \n\n\n \n Yi, e., Choudhary, R, Dzeng, e., Chang, e., Nguyen, e., Oloufa, e., Nassar, K, Malkawi, e., Srinivasan, e., Cheng, T., & others\n\n\n \n\n\n\n Automation in Construction, 14: 773–776. 2005.\n \n\n\n\n
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@article{yi2005j,\n  title = {J. {{Won Choi}} and {{J}}. {{Eun Hwang KotaView}}: Simulating Traditional {{Korean}} Architecture Interactively and Intelligently on the Web................................. 1 {{AM Tanyer}} and {{G}}. {{Aouad Moving}} beyond the Fourth Dimension with an {{IFC-based}} Single Project Database.......................................... 15},\n  author = {Yi, {\\relax YK} and Choudhary, R and Dzeng, {\\relax RJ} and Chang, {\\relax SY} and Nguyen, {\\relax TH} and Oloufa, {\\relax AA} and Nassar, K and Malkawi, {\\relax AM} and Srinivasan, {\\relax RS} and Cheng, T-m and others},\n  year = {2005},\n  journal = {Automation in Construction},\n  volume = {14},\n  pages = {773--776}\n}\n\n
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\n  \n 2004\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n A Hierarchical Optimization Framework for Simulation-Based Architectural Design.\n \n \n \n\n\n \n Choudhary, R.\n\n\n \n\n\n\n Ph.D. Thesis, University of Michigan, 2004.\n \n\n\n\n
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@phdthesis{choudhary2004hierarchical,\n  title = {A Hierarchical Optimization Framework for Simulation-Based Architectural Design},\n  author = {Choudhary, Ruchi},\n  year = {2004},\n  school = {University of Michigan}\n}\n\n
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\n \n\n \n \n \n \n \n The Role of the Physical Environment in the Hospital of the 21st Century: A Once-in-a-Lifetime Opportunity.\n \n \n \n\n\n \n Zimring, C., Joseph, A., & Choudhary, R.\n\n\n \n\n\n\n Concord, CA: The Center for Health Design, 311. 2004.\n \n\n\n\n
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@article{zimring2004role,\n  title = {The Role of the Physical Environment in the Hospital of the 21st Century: {{A}} Once-in-a-Lifetime Opportunity},\n  author = {Zimring, Craig and Joseph, Anjali and Choudhary, Ruchi},\n  year = {2004},\n  journal = {Concord, CA: The Center for Health Design},\n  volume = {311}\n}\n\n
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\n  \n 2003\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n The Design Analysis Integration (DAI) Initiative.\n \n \n \n\n\n \n Augenbroe, G., De Wilde, P., Moon, H. J., Malkawi, A., Brahme, R, & Choudhary, R\n\n\n \n\n\n\n In 8th IBPSA Conference, pages 79–86, 2003. \n \n\n\n\n
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@inproceedings{augenbroe2003design,\n  title = {The Design Analysis Integration ({{DAI}}) Initiative},\n  booktitle = {8th {{IBPSA}} Conference},\n  author = {Augenbroe, Godfried and De Wilde, Pieter and Moon, Hyeun Jun and Malkawi, Ali and Brahme, R and Choudhary, R},\n  year = {2003},\n  pages = {79--86}\n}\n\n
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\n \n\n \n \n \n \n \n Performance-Based Design Evolution: The Use of Genetic Algorithms and CFD.\n \n \n \n\n\n \n Malkawi, A. M, Srinivasan, R. S, Yi, Y. K., & Choudhary, R.\n\n\n \n\n\n\n Eighth International IBPSA. Eindhoven, Netherlands,793–798. 2003.\n \n\n\n\n
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@article{malkawi2003performance,\n  title = {Performance-Based Design Evolution: {{The}} Use of Genetic Algorithms and {{CFD}}},\n  author = {Malkawi, Ali M and Srinivasan, Ravi S and Yi, Yun Kyu and Choudhary, Ruchi},\n  year = {2003},\n  journal = {Eighth International IBPSA. Eindhoven, Netherlands},\n  pages = {793--798}\n}\n\n
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\n  \n 2002\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Architectural Layout Design Optimization.\n \n \n \n\n\n \n Michalek, J., Choudhary, R., & Papalambros, P.\n\n\n \n\n\n\n Engineering optimization, 34(5): 461–484. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{michalek2002architectural,\n  title = {Architectural Layout Design Optimization},\n  author = {Michalek, Jeremy and Choudhary, Ruchi and Papalambros, Panos},\n  year = {2002},\n  journal = {Engineering optimization},\n  volume = {34},\n  number = {5},\n  pages = {461--484},\n  publisher = {Taylor \\& Francis}\n}\n\n
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\n \n\n \n \n \n \n \n Integration of CFD and Genetic Algorithms.\n \n \n \n\n\n \n Choudhary, R., & Malkawi, A.\n\n\n \n\n\n\n In Proceedings of the Eighth International Conference on Air Distribution in Rooms, Copenhagen, Denmark, 2002. \n \n\n\n\n
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@inproceedings{choudhary2002integration,\n  title = {Integration of {{CFD}} and Genetic Algorithms},\n  booktitle = {Proceedings of the Eighth International Conference on Air Distribution in Rooms, Copenhagen, Denmark},\n  author = {Choudhary, Ruchi and Malkawi, Ali},\n  year = {2002}\n}\n\n
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\n  \n 2001\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n A Methodology for Micro-Level Building Thermal Analysis: Combining CFD and Experimental Set-Ups.\n \n \n \n\n\n \n Choudhary, R., & Malkawi, A.\n\n\n \n\n\n\n In Seventh International IBPSA Conference, pages 1275–1282, 2001. \n \n\n\n\n
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@inproceedings{choudhary2001methodology,\n  title = {A Methodology for Micro-Level Building Thermal Analysis: Combining {{CFD}} and Experimental Set-Ups},\n  booktitle = {Seventh International {{IBPSA}} Conference},\n  author = {Choudhary, Ruchi and Malkawi, Ali},\n  year = {2001},\n  pages = {1275--1282}\n}\n\n
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\n  \n 1999\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Visualizing the Sensed Environment in the Real World.\n \n \n \n\n\n \n Malkawi, A., & Choudhary, R.\n\n\n \n\n\n\n Journal of the Human-Environment System, 3(1): 61–69. 1999.\n \n\n\n\n
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@article{malkawi1999visualizing,\n  title = {Visualizing the Sensed Environment in the Real World},\n  author = {Malkawi, Ali and Choudhary, Ruchi},\n  year = {1999},\n  journal = {Journal of the Human-Environment System},\n  volume = {3},\n  number = {1},\n  pages = {61--69},\n  publisher = {Japanese Society of Human-Environment System}\n}\n\n
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\n  \n 1997\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Preparing International Proposals.\n \n \n \n\n\n \n Bartlett, R. E\n\n\n \n\n\n\n 1997.\n \n\n\n\n
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@misc{bartlett1997preparing,\n  title = {Preparing International Proposals},\n  author = {Bartlett, Robert E},\n  year = {1997},\n  publisher = {T. Telford}\n}\n\n
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\n \n\n \n \n \n \n \n Estimation of Natural Ventilation Parameters Using a Bayesian Approach.\n \n \n \n\n\n \n Choi, W., Kikumoto, H., Choudhary, R., & Ooka, R.\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{choiestimation,\n  title = {Estimation of Natural Ventilation Parameters Using a {{Bayesian}} Approach},\n  author = {Choi, Wonjun and Kikumoto, Hideki and Choudhary, Ruchi and Ooka, Ryozo}\n}\n\n
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\n \n\n \n \n \n \n \n Cambridge and Bangalore District Models.\n \n \n \n\n\n \n Pickering, e., & Choudhary, R\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{pickeringcambridge,\n  title = {Cambridge and {{Bangalore}} District Models},\n  author = {Pickering, {\\relax BC} and Choudhary, R}\n}\n\n
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\n \n\n \n \n \n \n \n Simulation of a Ground Source Heat Pump System for Simultaneous Heating and Cooling.\n \n \n \n\n\n \n Loizide, S., Menberg, K., & Choudhary, R.\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{loizidesimulation,\n  title = {Simulation of a Ground Source Heat Pump System for Simultaneous Heating and Cooling},\n  author = {Loizide, Stephanie and Menberg, Kathrin and Choudhary, Ruchi}\n}\n\n
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\n \n\n \n \n \n \n \n Integrating Hydroponics into Office Buildings: Impacts of Plants on the Building Environment and Office Occupants.\n \n \n \n\n\n \n Jans-Singh, M., Ward, R., Gillard, H., & Choudhary, R.\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{jansintegrating,\n  title = {Integrating Hydroponics into Office Buildings: {{Impacts}} of Plants on the Building Environment and Office Occupants},\n  author = {{Jans-Singh}, Melanie and Ward, Rebecca and Gillard, Helen and Choudhary, Ruchi}\n}\n\n
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\n \n\n \n \n \n \n \n Continuous Calibration of a Digital Twin; a Particle Filter Approach.\n \n \n \n\n\n \n Ward, R., Choudhary, R., Gregory, A., & Girolami, M.\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{wardcontinuous,\n  title = {Continuous Calibration of a Digital Twin; a Particle Filter Approach},\n  author = {Ward, Rebecca and Choudhary, Ruchi and Gregory, Alastair and Girolami, Mark}\n}\n\n
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