A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings
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- Sung Hoon Yoon & Jonghoon Ahn, 2020. "Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season," Energies, MDPI, vol. 13(22), pages 1-15, November.
- Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
- Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
- Jamer Jiménez Mares & Loraine Navarro & Christian G. Quintero M. & Mauricio Pardo, 2020. "A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques," Energies, MDPI, vol. 13(16), pages 1-16, August.
- Tingchen Fang & Yiming Zhao & Jian Gong & Feiliang Wang & Jian Yang, 2021. "Investigation on Maintenance Technology of Large-Scale Public Venues Based on BIM Technology," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
- 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).
- Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
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Keywords
intelligent operation and maintenance; large public building; digital twin; artificial intelligence; building sustainability;All these keywords.
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