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Data science for building energy management: A review

Citations

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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. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
  3. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  4. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
  5. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
  6. Šuklje, Tomaž & Hamdy, Mohamed & Arkar, Ciril & Hensen, Jan L.M. & Medved, Sašo, 2019. "An inverse modeling approach for the thermal response modeling of green façades," Applied Energy, Elsevier, vol. 235(C), pages 1447-1456.
  7. Balaji, Bharathan & Bhattacharya, Arka & Fierro, Gabriel & Gao, Jingkun & Gluck, Joshua & Hong, Dezhi & Johansen, Aslak & Koh, Jason & Ploennigs, Joern & Agarwal, Yuvraj & Bergés, Mario & Culler, Davi, 2018. "Brick : Metadata schema for portable smart building applications," Applied Energy, Elsevier, vol. 226(C), pages 1273-1292.
  8. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
  9. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
  10. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
  11. Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
  12. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
  13. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
  14. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
  15. Luo, Na & Pritoni, Marco & Hong, Tianzhen, 2021. "An overview of data tools for representing and managing building information and performance data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  16. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
  17. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
  18. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
  19. Ma, Minda & Cai, Wei & Cai, Weiguang, 2018. "Carbon abatement in China's commercial building sector: A bottom-up measurement model based on Kaya-LMDI methods," Energy, Elsevier, vol. 165(PA), pages 350-368.
  20. Ahlrichs, Jakob & Wenninger, Simon & Wiethe, Christian & Häckel, Björn, 2022. "Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach," Energy Policy, Elsevier, vol. 160(C).
  21. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
  22. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
  23. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  24. Pasichnyi, Oleksii & Wallin, Jörgen & Levihn, Fabian & Shahrokni, Hossein & Kordas, Olga, 2019. "Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?," Energy Policy, Elsevier, vol. 127(C), pages 486-499.
  25. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  26. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.
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