IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9680-d1172916.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9680/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
    2. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    3. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    4. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    6. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    7. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    8. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
    9. Davor Stjelja & Juha Jokisalo & Risto Kosonen, 2022. "Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor," Energies, MDPI, vol. 15(6), pages 1-21, March.
    10. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    11. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    12. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    13. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    14. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    15. Haiyan Meng & Yakai Lu & Zhe Tian & Xiangbei Jiang & Zhongqing Han & Jide Niu, 2023. "Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study," Energies, MDPI, vol. 16(14), pages 1-19, July.
    16. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    17. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    18. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2024. "Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review," Mathematics, MDPI, vol. 12(21), pages 1-51, October.
    19. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    20. Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9680-:d:1172916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.