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Power consumption prediction of variable refrigerant flow system through data-physics hybrid approach: An online prediction test in office building

Author

Listed:
  • Yue, Bao
  • Wei, Ziqing
  • Zheng, Chunyuan
  • Ding, Yunxiao
  • Li, Bin
  • Li, Dongdong
  • Liang, Xingang
  • Zhai, Xiaoqiang

Abstract

Variable refrigerant flow (VRF) system contains numerous sensors and has the advance for fast response, which is suitable for building demand response (DR) management. Fast and accurate power consumption prediction of VRF system is essential for DR. As traditional prediction methods, white-box models are difficult to build on operational data, while black-box models cannot make interpretable predictions. Neither of them can meet the requirements of power consumption prediction for VRF system under demand response. Therefore, a grey box model for power consumption of VRF system is proposed in this study, which has the advantage of data-driven and interpretability.

Suggested Citation

  • Yue, Bao & Wei, Ziqing & Zheng, Chunyuan & Ding, Yunxiao & Li, Bin & Li, Dongdong & Liang, Xingang & Zhai, Xiaoqiang, 2023. "Power consumption prediction of variable refrigerant flow system through data-physics hybrid approach: An online prediction test in office building," Energy, Elsevier, vol. 278(PA).
  • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012203
    DOI: 10.1016/j.energy.2023.127826
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    References listed on IDEAS

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    1. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    2. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
    3. Wang, Junke & Jiang, Yilin & Tang, Choon Yik & Song, Li, 2022. "Development and validation of a second-order thermal network model for residential buildings," Applied Energy, Elsevier, vol. 306(PB).
    4. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    5. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    6. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    7. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    8. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.
    9. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    10. Ding, Yan & Lyu, Yacong & Lu, Shilei & Wang, Ran, 2022. "Load shifting potential assessment of building thermal storage performance for building design," Energy, Elsevier, vol. 243(C).
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