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Grading buildings on energy performance using city benchmarking data

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Cited by:

  1. Lee, Ruda & Kim, Dongsu & Yoon, Jongho & Kang, Eunho & Cho, Heejin & Kim, Jinhwi, 2024. "Development and calibration of apartment building energy model based on architectural and energy consumption characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
  2. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
  3. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  4. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  5. Lee, Kyungjae & Lim, Hyunwoo & Hwang, Jeongyun & Lee, Doyeon, 2024. "Evaluating missing data handling methods for developing building energy benchmarking models," Energy, Elsevier, vol. 308(C).
  6. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
  7. Gao, Yuan & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & Liu, Mingzhe & Xu, Tingting & Ruan, Yingjun & Shang, Juan, 2025. "Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems," Applied Energy, Elsevier, vol. 384(C).
  8. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
  9. Yuan Chen & Genglong He & Yuan Fang & Dongxu Li & Xi Wang, 2025. "Carbon Emission Evaluation System for Foundation Construction Based on Entropy–TOPSIS and K-Means Methods," Sustainability, MDPI, vol. 17(1), pages 1-34, January.
  10. Ma, Nan & Waegel, Alex & Hakkarainen, Max & Braham, William W. & Glass, Lior & Aviv, Dorit, 2023. "Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces," Applied Energy, Elsevier, vol. 332(C).
  11. Quan, Steven Jige & Xue, Yang & Li, Chaosu, 2025. "Nonlinearity in the relationships between urban form and residential energy use intensity," Applied Energy, Elsevier, vol. 383(C).
  12. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
  13. Tahir Mahmood & Muhammad Asif, 2024. "Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms," Energies, MDPI, vol. 17(19), pages 1-17, October.
  14. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  15. Yu, Yinyun & Li, Congdong & Fu, Yelin & Yang, Weiming, 2023. "A group decision-making method to measure national energy architecture performance: A case study of the International energy Agency," Applied Energy, Elsevier, vol. 330(PA).
  16. Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
  17. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
  18. Piscitelli, Marco Savino & Giudice, Rocco & Capozzoli, Alfonso, 2024. "A holistic time series-based energy benchmarking framework for applications in large stocks of buildings," Applied Energy, Elsevier, vol. 357(C).
  19. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
  20. Gómez, Patricia & Shaikh, Nazrul I. & Erkoc, Murat, 2024. "Continuous improvement in the efficient use of energy in office buildings through peers effects," Applied Energy, Elsevier, vol. 360(C).
  21. Liu, Xue & Ding, Yong & Tang, Hao & Fan, Lingxiao & Lv, Jie, 2022. "Investigating the effects of key drivers on energy consumption of nonresidential buildings: A data-driven approach integrating regularization and quantile regression," Energy, Elsevier, vol. 244(PA).
  22. 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).
  23. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
  24. Sarah Barns, 2021. "Out of the loop? On the radical and the routine in urban big data," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3203-3210, November.
  25. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
  26. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
  27. Chunyan Wang & Hanying Jiang & Hao Wu & Yi Liu & Siyue Guo & Ming Xu, 2023. "Scaling in urban building energy use and its influencing factors," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1076-1088, August.
  28. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
  29. Abdulaziz Alghamdi & Guangji Hu & Husnain Haider & Kasun Hewage & Rehan Sadiq, 2020. "Benchmarking of Water, Energy, and Carbon Flows in Academic Buildings: A Fuzzy Clustering Approach," Sustainability, MDPI, vol. 12(11), pages 1-25, May.
  30. Tahmineh Ladi & Shaghayegh Jabalameli & Ayyoob Sharifi, 2022. "Applications of machine learning and deep learning methods for climate change mitigation and adaptation," Environment and Planning B, , vol. 49(4), pages 1314-1330, May.
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