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\n  \n 2025\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Representation Vector-Based Time Series Similarity Analysis Using Unsupervised Contrastive Learning Time Series Encoder.\n \n \n \n \n\n\n \n Choi, W., Lee, S., Langtry, M., & Choudhary, R.\n\n\n \n\n\n\n Korean Journal of Air-Conditioning and Refrigeration Engineering, 37(2): 72–81. February 2025.\n \n\n\n\n
\n\n\n\n \n \n \"RepresentationPaper\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{choi2025RepresentationVectorBasedTime,\n  title = {{Representation Vector-Based Time Series Similarity Analysis Using Unsupervised Contrastive Learning Time Series Encoder}},\n  author = {Choi, Wonjun and Lee, Sangwon and Langtry, Max and Choudhary, Ruchi},\n  year = {2025},\n  month = feb,\n  journal = {Korean Journal of Air-Conditioning and Refrigeration Engineering},\n  volume = {37},\n  number = {2},\n  pages = {72--81},\n  issn = {1229-6422},\n  doi = {10.6110/KJACR.2025.37.2.72},\n  url = {https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE12036797},\n  urldate = {2025-03-10},\n  abstract = {Data scarcity and high development costs pose significant challenges to building-specific energy demand forecasting models. To address these issues, this study introduces a time series similarity assessment method that utilizes TS2Vec, an unsupervised learning-based encoder for extracting time series representation vectors. The efficacy of this approach is demonstrated using anonymized datasets of building electricity usage from Cambridge, UK. The proposed methodology stands out for its ability to identify high-similarity data segments by flexibly adjusting the evaluation time window used for extracting representation vectors, outperforming traditional average similarity assessments. Principal component analysis was employed for dimensionality reduction and visualization, alongside a moving window cosine similarity approach to enhance the interpretability of complex multivariate time series data similarities. The study's key findings are as follows. First, dynamic similarity analysis effectively captured the complexity of building energy use patterns. Second, the approach demonstrated the potential to optimize transfer learning by automatically identifying the most suitable source data. Third, the study explored the feasibility of employing dynamic model selection and ensemble techniques based on temporal similarity changes. This study proposes a practical and scalable methodology to mitigate data scarcity and reduce model development costs, thereby facilitating more efficient, adaptive, and accurate energy demand forecasting.},\n  copyright = {All rights reserved},\n  langid = {korean}\n}\n\n
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\n Data scarcity and high development costs pose significant challenges to building-specific energy demand forecasting models. To address these issues, this study introduces a time series similarity assessment method that utilizes TS2Vec, an unsupervised learning-based encoder for extracting time series representation vectors. The efficacy of this approach is demonstrated using anonymized datasets of building electricity usage from Cambridge, UK. The proposed methodology stands out for its ability to identify high-similarity data segments by flexibly adjusting the evaluation time window used for extracting representation vectors, outperforming traditional average similarity assessments. Principal component analysis was employed for dimensionality reduction and visualization, alongside a moving window cosine similarity approach to enhance the interpretability of complex multivariate time series data similarities. The study's key findings are as follows. First, dynamic similarity analysis effectively captured the complexity of building energy use patterns. Second, the approach demonstrated the potential to optimize transfer learning by automatically identifying the most suitable source data. Third, the study explored the feasibility of employing dynamic model selection and ensemble techniques based on temporal similarity changes. This study proposes a practical and scalable methodology to mitigate data scarcity and reduce model development costs, thereby facilitating more efficient, adaptive, and accurate energy demand forecasting.\n
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\n \n\n \n \n \n \n \n \n Self-Attention Variational Autoencoder-Based Method for Incomplete Model Parameter Imputation of Digital Twin Building Energy Systems.\n \n \n \n \n\n\n \n Lu, J., Zhang, C., Li, B., Zhao, Y., Choudhary, R., & Langtry, M.\n\n\n \n\n\n\n Energy and Buildings, 328: 115162. February 2025.\n \n\n\n\n
\n\n\n\n \n \n \"Self-AttentionPaper\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{lu2025SelfattentionVariationalAutoencoderbased,\n  title = {Self-Attention Variational Autoencoder-Based Method for Incomplete Model Parameter Imputation of Digital Twin Building Energy Systems},\n  author = {Lu, Jie and Zhang, Chaobo and Li, Bozheng and Zhao, Yang and Choudhary, Ruchi and Langtry, Max},\n  year = {2025},\n  month = feb,\n  journal = {Energy and Buildings},\n  volume = {328},\n  pages = {115162},\n  issn = {0378-7788},\n  doi = {10.1016/j.enbuild.2024.115162},\n  url = {https://www.sciencedirect.com/science/article/pii/S0378778824012787},\n  urldate = {2025-03-02},\n  abstract = {Digital twin models serve as the most crucial foundation for estimating the expected conservation effects of building energy systems. The establishment of digital twin models relies heavily on the detailed design parameters of building energy systems, including component topology, power, and efficiency. However, a considerable number of existing buildings lack complete modeling parameters. It is challenging to impute the incomplete parameters for building energy systems, since the different building energy systems have different modeling parameters and different amounts of incomplete parameters. To address this challenge, a self-attention variational autoencoder-based method is proposed. Its basic idea is to employ the self-attention mechanism to identify the key parameters within the entire parameters, and to learn the relationships between the incomplete parameters and the key parameters. The method comprises three steps: feature embedding, model building, and supervised learning. The feature embedding is employed to represent the features of building energy systems, with each feature carrying a physical meaning. A self-attention variational autoencoder model is then applied to impute the incomplete parameters based on the available parameters and the relationship between the incomplete parameters and the key parameters. Supervised learning is adopted to train the model based on the principles of the evidence lower bound. The evaluations are conducted using 350 various practical building energy systems sourced from the excellent design atlas in China. Three representative incomplete parameter imputation methods are selected as baseline models for performance comparison. The results indicate that the proposed method exhibits high imputation accuracy, good flexibility, and good interpretability.},\n  copyright = {All rights reserved},\n  keywords = {Digital twin,Feature embedding,Incomplete model parameter imputation,Multi-head self-attention,Variational autoencoder}\n}\n\n
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\n Digital twin models serve as the most crucial foundation for estimating the expected conservation effects of building energy systems. The establishment of digital twin models relies heavily on the detailed design parameters of building energy systems, including component topology, power, and efficiency. However, a considerable number of existing buildings lack complete modeling parameters. It is challenging to impute the incomplete parameters for building energy systems, since the different building energy systems have different modeling parameters and different amounts of incomplete parameters. To address this challenge, a self-attention variational autoencoder-based method is proposed. Its basic idea is to employ the self-attention mechanism to identify the key parameters within the entire parameters, and to learn the relationships between the incomplete parameters and the key parameters. The method comprises three steps: feature embedding, model building, and supervised learning. The feature embedding is employed to represent the features of building energy systems, with each feature carrying a physical meaning. A self-attention variational autoencoder model is then applied to impute the incomplete parameters based on the available parameters and the relationship between the incomplete parameters and the key parameters. Supervised learning is adopted to train the model based on the principles of the evidence lower bound. The evaluations are conducted using 350 various practical building energy systems sourced from the excellent design atlas in China. Three representative incomplete parameter imputation methods are selected as baseline models for performance comparison. The results indicate that the proposed method exhibits high imputation accuracy, good flexibility, and good interpretability.\n
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\n \n\n \n \n \n \n \n \n Quantifying the Benefit of Load Uncertainty Reduction for the Design of District Energy Systems under Grid Constraints Using the Value of Information.\n \n \n \n \n\n\n \n Langtry, M., & Choudhary, R.\n\n\n \n\n\n\n Applied Energy, 400: 126549. December 2025.\n \n\n\n\n
\n\n\n\n \n \n \"QuantifyingPaper\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{langtry2025QuantifyingBenefitLoad,\n  title = {Quantifying the Benefit of Load Uncertainty Reduction for the Design of District Energy Systems under Grid Constraints Using the Value of Information},\n  author = {Langtry, Max and Choudhary, Ruchi},\n  year = {2025},\n  month = dec,\n  journal = {Applied Energy},\n  volume = {400},\n  pages = {126549},\n  issn = {0306-2619},\n  doi = {10.1016/j.apenergy.2025.126549},\n  url = {https://www.sciencedirect.com/science/article/pii/S0306261925012796},\n  urldate = {2025-08-04},\n  abstract = {Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for improved designs with less compromise to be identified, reducing the cost of decarbonizing energy usage. However, the building monitoring required to reduce load uncertainty is costly. This study quantifies the economic benefit of practical building monitoring for supporting energy system design decisions, to determine if its benefits outweigh its cost. It uses an extension of the Value of Information analysis (VoI) framework, called `On-Policy' VoI, which analyses the benefit of uncertainty reduction for complex decision making tasks where decision policies are required. This is applied to a case study district energy system design problem, where a Linear Program model is used to size solar-battery systems and grid connection capacity under uncertain building loads, modelled using historic electricity metering data. Load uncertainty is found to significantly impact both system operating costs ({\\textpm}30\\%) and the optimal system design ({\\textpm}20\\%). However, using building monitoring data to improve the design of the district reduces overall costs by less than 1.5\\% on average. As this is less than the cost of measurement, using monitoring is not economically worthwhile in this case. This provides the first numerical evidence to support the sufficiency of using standard building load profiles for energy system design. Further, reducing only uncertainty in mean load is found to provide most of the available decision support benefit, meaning using hourly measurement data provides little benefit for energy retrofit design.},\n  keywords = {Building monitoring,District energy system,Energy planning,Stochastic programming,Uncertainty reduction,Value of information}\n}\n\n
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\n Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for improved designs with less compromise to be identified, reducing the cost of decarbonizing energy usage. However, the building monitoring required to reduce load uncertainty is costly. This study quantifies the economic benefit of practical building monitoring for supporting energy system design decisions, to determine if its benefits outweigh its cost. It uses an extension of the Value of Information analysis (VoI) framework, called `On-Policy' VoI, which analyses the benefit of uncertainty reduction for complex decision making tasks where decision policies are required. This is applied to a case study district energy system design problem, where a Linear Program model is used to size solar-battery systems and grid connection capacity under uncertain building loads, modelled using historic electricity metering data. Load uncertainty is found to significantly impact both system operating costs (\\textpm30%) and the optimal system design (\\textpm20%). However, using building monitoring data to improve the design of the district reduces overall costs by less than 1.5% on average. As this is less than the cost of measurement, using monitoring is not economically worthwhile in this case. This provides the first numerical evidence to support the sufficiency of using standard building load profiles for energy system design. Further, reducing only uncertainty in mean load is found to provide most of the available decision support benefit, meaning using hourly measurement data provides little benefit for energy retrofit design.\n
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\n \n\n \n \n \n \n \n \n The Value of Hedging against Energy Storage Uncertainties When Designing Energy Parks.\n \n \n \n \n\n\n \n Langtry, M., & Choudhary, R.\n\n\n \n\n\n\n Energy, 334: 137600. October 2025.\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 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 
@article{langtry2025ValueHedgingEnergya,\n  title = {The Value of Hedging against Energy Storage Uncertainties When Designing Energy Parks},\n  author = {Langtry, Max and Choudhary, Ruchi},\n  year = {2025},\n  month = oct,\n  journal = {Energy},\n  volume = {334},\n  pages = {137600},\n  issn = {0360-5442},\n  doi = {10.1016/j.energy.2025.137600},\n  url = {https://www.sciencedirect.com/science/article/pii/S0360544225032426},\n  urldate = {2025-07-29},\n  abstract = {Energy storage is needed to match renewable generation to industrial loads in energy parks. However, the future performance of bulk storage technologies is currently highly uncertain. Due to the urgency of decarbonization targets, energy park projects must be designed and begun now. But, as uncertainty in storage performance reduces, a different technology than identified during initial design may turn out cheaper. Enabling flexibility so that designs can be updated as better information becomes available would lower the cost of decarbonizing industry. But having this flexibility is itself costly. This raises the question, ``Is it worth it?'' This study quantifies the benefit of retaining flexibility to adapt energy park designs and optionality over storage technology choice as uncertainty reduces, to determine whether it is economically worthwhile. It applies the Value of Information analysis framework to the sizing of wind, solar, and storage in an illustrative energy park model based on a real-world proposal near Rotterdam, considering uncertainty in storage efficiency, lifetime, and capital cost. Updating asset sizings after storage uncertainty reduced is found to reduce total costs by 18\\% on average. Having the option to switch storage technology choice as well reduces costs by a further 13\\%, which is substantially greater than the cost of providing storage optionality. Using two storage technologies in the energy park reduces costs by 14\\%, and in this case storage optionality is not worthwhile. These results are robust to the level of uncertainty reduction in storage performance, and the risk aversion of the system designer.},\n  keywords = {Conditional Value-at-Risk,Energy planning,Energy storage,Park integrated energy system,Stochastic Programming,Uncertainty reduction,Value of Information}\n}\n\n
\n
\n\n\n
\n Energy storage is needed to match renewable generation to industrial loads in energy parks. However, the future performance of bulk storage technologies is currently highly uncertain. Due to the urgency of decarbonization targets, energy park projects must be designed and begun now. But, as uncertainty in storage performance reduces, a different technology than identified during initial design may turn out cheaper. Enabling flexibility so that designs can be updated as better information becomes available would lower the cost of decarbonizing industry. But having this flexibility is itself costly. This raises the question, ``Is it worth it?'' This study quantifies the benefit of retaining flexibility to adapt energy park designs and optionality over storage technology choice as uncertainty reduces, to determine whether it is economically worthwhile. It applies the Value of Information analysis framework to the sizing of wind, solar, and storage in an illustrative energy park model based on a real-world proposal near Rotterdam, considering uncertainty in storage efficiency, lifetime, and capital cost. Updating asset sizings after storage uncertainty reduced is found to reduce total costs by 18% on average. Having the option to switch storage technology choice as well reduces costs by a further 13%, which is substantially greater than the cost of providing storage optionality. Using two storage technologies in the energy park reduces costs by 14%, and in this case storage optionality is not worthwhile. These results are robust to the level of uncertainty reduction in storage performance, and the risk aversion of the system designer.\n
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\n \n\n \n \n \n \n \n \n Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts.\n \n \n \n \n\n\n \n Raisch, F., Langtry, M., Koch, F., Choudhary, R., Goebel, C., & Tischler, B.\n\n\n \n\n\n\n August 2025.\n \n\n\n\n
\n\n\n\n \n \n \"AdaptingPaper\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 
@misc{raisch2025AdaptingChangeComparison,\n  title = {Adapting to {{Change}}: {{A Comparison}} of {{Continual}} and {{Transfer Learning}} for {{Modeling Building Thermal Dynamics}} under {{Concept Drifts}}},\n  shorttitle = {Adapting to {{Change}}},\n  author = {Raisch, Fabian and Langtry, Max and Koch, Felix and Choudhary, Ruchi and Goebel, Christoph and Tischler, Benjamin},\n  year = {2025},\n  month = aug,\n  number = {arXiv:2508.21615},\n  eprint = {2508.21615},\n  primaryclass = {eess},\n  publisher = {arXiv},\n  address = {(Submitted to Energy and Buildings)},\n  doi = {10.48550/arXiv.2508.21615},\n  url = {http://arxiv.org/abs/2508.21615},\n  urldate = {2025-09-01},\n  abstract = {Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine Learning literature, Continual Learning (CL) methods are used to update models of changing systems. TL approaches can also address this challenge by reusing the pretrained model at each update step and fine-tuning it with new measurement data. A comprehensive study on how to incorporate new measurement data over time to improve prediction accuracy and address the challenges of concept drifts (changes in dynamics) for building thermal dynamics is still missing. Therefore, this study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation. The methods are evaluated using 5--7 years of simulated data representative of single-family houses in Central Europe, including scenarios with concept drifts from retrofits and changes in occupancy. We propose a CL strategy (Seasonal Memory Learning) that provides greater accuracy improvements than existing CL and TL methods, while maintaining low computational effort. SML outperformed the benchmark of initial fine-tuning by 28.1{\\textbackslash}\\% without concept drifts and 34.9{\\textbackslash}\\% with concept drifts.},\n  archiveprefix = {arXiv},\n  keywords = {Computer Science - Machine Learning,Computer Science - Systems and Control,Electrical Engineering and Systems Science - Systems and Control}\n}
\n
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\n Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine Learning literature, Continual Learning (CL) methods are used to update models of changing systems. TL approaches can also address this challenge by reusing the pretrained model at each update step and fine-tuning it with new measurement data. A comprehensive study on how to incorporate new measurement data over time to improve prediction accuracy and address the challenges of concept drifts (changes in dynamics) for building thermal dynamics is still missing. Therefore, this study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation. The methods are evaluated using 5–7 years of simulated data representative of single-family houses in Central Europe, including scenarios with concept drifts from retrofits and changes in occupancy. We propose a CL strategy (Seasonal Memory Learning) that provides greater accuracy improvements than existing CL and TL methods, while maintaining low computational effort. SML outperformed the benchmark of initial fine-tuning by 28.1\\% without concept drifts and 34.9\\% with concept drifts.\n
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\n  \n 2024\n \n \n (3)\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 April 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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{langtry2024CambridgeUniversityEstates,\n  title = {Cambridge {{University Estates}} Building Energy Usage Archive},\n  author = {Langtry, Max and Choudhary, Ruchi},\n  year = {2024},\n  month = apr,\n  doi = {10.5281/zenodo.10955332},\n  url = {https://github.com/EECi/Cambridge-Estates-Building-Energy-Archive},\n  urldate = {2024-04-10},\n  abstract = {Data archive for Cambridge University Estates building energy usage dataset (for use with CityLearn)},\n  copyright = {MIT}\n}\n\n
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\n Data archive for Cambridge University Estates building energy usage dataset (for use with CityLearn)\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., Xuereb Conti, Z., & Choudhary, R.\n\n\n \n\n\n\n Energy and Buildings, 320: 114605. October 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
\n 
@article{langtry2024ImpactDataForecasting,\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 Xuereb Conti, Zack and Choudhary, Ruchi},\n  year = {2024},\n  month = oct,\n  journal = {Energy and Buildings},\n  volume = {320},\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  urldate = {2024-08-06},\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 is 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\\% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.},\n  copyright = {All rights reserved},\n  keywords = {Building energy optimization,Data requirements,Energy storage,Historical data,Machine learning,Model predictive control (MPC),Time-series forecasting}\n}\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 is 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% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.\n
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\n \n\n \n \n \n \n \n \n Rationalising Data Collection for Supporting Decision Making in Building Energy Systems Using Value of Information 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 Journal of Building Performance Simulation,1–17. November 2024.\n \n\n\n\n
\n\n\n\n \n \n \"RationalisingPaper\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{langtry2024RationalisingDataCollection,\n  title = {Rationalising Data Collection for Supporting Decision Making in Building Energy Systems Using Value of Information Analysis},\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 = {2024},\n  month = nov,\n  journal = {Journal of Building Performance Simulation},\n  pages = {1--17},\n  publisher = {Taylor \\& Francis},\n  issn = {1940-1493},\n  doi = {10.1080/19401493.2024.2423827},\n  url = {https://doi.org/10.1080/19401493.2024.2423827},\n  urldate = {2025-01-06},\n  abstract = {The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economized and prioritised, avoiding wastage.},\n  copyright = {All rights reserved},\n  keywords = {Bayesian decision analysis,Building energy management,Data collection,Uncertainty quantification,Value of Information}\n}\n\n
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\n The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economized and prioritised, avoiding wastage.\n
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\n  \n 2023\n \n \n (3)\n \n \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  copyright = {All rights reserved},\n  keywords = {Statistics - Applications}\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 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, pages 3483–3490, 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  series = {Building {{Simulation}}},\n  volume = {18},\n  pages = {3483--3490},\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  copyright = {All rights reserved},\n  langid = {american}\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 The CityLearn Challenge 2023, NeurIPS.\n \n \n \n \n\n\n \n Nagy, Z., Nweye, K., Mohanty, S., Choudhary, R., Langtry, M., Henze, G., Drgona, J., Dey, S., Capozzoli, A., & Ouf, M.\n\n\n \n\n\n\n December 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\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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@misc{nagy2023CityLearnChallenge2023,\n  title = {The {{CityLearn Challenge}} 2023, {{NeurIPS}}},\n  author = {Nagy, Zoltan and Nweye, Kingsley and Mohanty, Sharada and Choudhary, Ruchi and Langtry, Max and Henze, Gregor and Drgona, Jan and Dey, Sourav and Capozzoli, Alfonso and Ouf, Mohamed},\n  year = {2023},\n  month = dec,\n  journal = {SlidesLive},\n  url = {https://neurips.cc/virtual/2023/competition/66590},\n  urldate = {2025-03-03},\n  abstract = {Professional Conference Recording},\n  copyright = {All rights reserved},\n  langid = {american},\n  annotation = {NeurIPS}\n}\n\n
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\n Professional Conference Recording\n
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