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Grading buildings on energy performance using city benchmarking data

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  1. 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.
  2. 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).
  3. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
  4. 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).
  5. 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.
  6. 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).
  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. 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).
  9. 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).
  10. 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.
  11. 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.
  12. 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.
  13. 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).
  14. 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).
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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).
  20. 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).
  21. 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).
  22. 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).
  23. 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).
  24. 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).
  25. 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.
  26. 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).
  27. 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).
  28. 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).
  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. 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).
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