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\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
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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 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 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
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@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 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
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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 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
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\n\n \n \n 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 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 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
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\n\n \n \n 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 \n\n\n\n
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@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
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\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 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
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\n\n \n \n 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 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
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\n\n \n \n 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
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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 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
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\n\n \n \n Paper\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 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
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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 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
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\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 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
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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 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
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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 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
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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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