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

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

Energy, 2023, vol. 278, issue PA

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.

Keywords: Power consumption prediction; Data-driven physics-hybrid; Building thermal load; Variable refrigerant flow system (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012203

DOI: 10.1016/j.energy.2023.127826

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