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A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings

Author

Listed:
  • Zedong Jiao

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Xiuli Du

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Zhansheng Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Liang Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Zhe Sun

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Guoliang Shi

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Ruirui Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China)

Abstract

This article aims to systematically summarize the methods for intelligent operation of large public buildings, the integration and application of related technologies, as well as their development trends and challenges. (1) Background: In response to the rapid development and future needs of intelligent operation and maintenance, this study summarizes the development process of intelligent operation and maintenance in building operations, as well as relevant technical achievements and challenges; (2) Method: Quantitative and qualitative bibliometric statistical methods were used for overall analysis; (3) Result: Based on system theory, a B-IRO model was developed, and the current status of intelligent operation- and maintenance-related technologies and applications was sorted out. A framework for the entire industry was established, and future development trends were proposed as further research directions.

Suggested Citation

  • Zedong Jiao & Xiuli Du & Zhansheng Liu & Liang Liu & Zhe Sun & Guoliang Shi & Ruirui Liu, 2023. "A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9680-:d:1172916
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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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