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Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

Citations

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  1. Brett Bass & Joshua New & William Copeland, 2020. "Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area," Energies, MDPI, vol. 14(1), pages 1-23, December.
  2. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
  3. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
  4. Rogeau, A. & Girard, R. & Abdelouadoud, Y. & Thorel, M. & Kariniotakis, G., 2020. "Joint optimization of building-envelope and heating-system retrofits at territory scale to enhance decision-aiding," Applied Energy, Elsevier, vol. 264(C).
  5. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
  6. Yang, Xining & Hu, Mingming & Heeren, Niko & Zhang, Chunbo & Verhagen, Teun & Tukker, Arnold & Steubing, Bernhard, 2020. "A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation," Applied Energy, Elsevier, vol. 280(C).
  7. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
  8. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
  9. Song, Jeonghun & Song, Seung Jin, 2020. "A framework for analyzing city-wide impact of building-integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
  10. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
  11. Chévez, Pedro Joaquín & Martini, Irene & Discoli, Carlos, 2019. "Methodology developed for the construction of an urban-energy diagnosis aimed to assess alternative scenarios: An intra-urban approach to foster cities’ sustainability," Applied Energy, Elsevier, vol. 237(C), pages 751-778.
  12. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
  13. Bass, Brett & New, Joshua & Clinton, Nicholas & Adams, Mark & Copeland, Bill & Amoo, Charles, 2022. "How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling," Applied Energy, Elsevier, vol. 327(C).
  14. Quan, Steven Jige & Li, Chaosu, 2021. "Urban form and building energy use: A systematic review of measures, mechanisms, and methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
  15. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
  16. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
  17. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
  18. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2019. "Development of a GIS-based platform for the allocation and optimisation of distributed storage in urban energy systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  19. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
  20. Silverio HERNANDEZ-MORENO, 2019. "International Experiences On The Implementation Of Public Policies For Urban Planning To Face Climate Change," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 14(2), pages 72-88, May.
  21. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  22. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Tonello, Andrea M. & Qadrdan, Meysam & Wu, Jianzhong, 2022. "Data-driven prosumer-centric energy scheduling using convolutional neural networks," Applied Energy, Elsevier, vol. 308(C).
  23. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
  24. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  25. Qiu, Yu & Xu, Yucong & Li, Qing & Wang, Jikang & Wang, Qiliang & Liu, Bin, 2021. "Efficiency enhancement of a solar trough collector by combining solar and hot mirrors," Applied Energy, Elsevier, vol. 299(C).
  26. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
  27. Nutkiewicz, Alex & Jain, Rishee K. & Bardhan, Ronita, 2018. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India," Applied Energy, Elsevier, vol. 231(C), pages 433-445.
  28. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  29. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
  30. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  31. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
  32. Chang, Soowon & Saha, Nirvik & Castro-Lacouture, Daniel & Yang, Perry Pei-Ju, 2019. "Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling," Applied Energy, Elsevier, vol. 249(C), pages 253-264.
  33. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
  34. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
  35. Ranalli, Joseph & Alhamwi, Alaa, 2020. "Configurations of renewable power generation in cities using open source approaches: With Philadelphia case study," Applied Energy, Elsevier, vol. 269(C).
  36. Wang, Wei & Liu, Ke & Zhang, Muxing & Shen, Yuchi & Jing, Rui & Xu, Xiaodong, 2021. "From simulation to data-driven approach: A framework of integrating urban morphology to low-energy urban design," Renewable Energy, Elsevier, vol. 179(C), pages 2016-2035.
  37. Chao Ding & Nan Zhou, 2020. "Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study," Energies, MDPI, vol. 13(12), pages 1-16, June.
  38. Nutkiewicz, Alex & Mastrucci, Alessio & Rao, Narasimha D. & Jain, Rishee K., 2022. "Cool roofs can mitigate cooling energy demand for informal settlement dwellers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  39. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
  40. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
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