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

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. The proposed model consists of four sub-models, which predict the building thermal load, compressor frequency, high pressure state and low pressure state of VRF, respectively. These predictions are finally used as inputs to the power prediction model. The proposed model is verified on both offline test and online test. The results show that the model is capable of predicting the power consumption accurately under high time resolution. During the online test, the MAE, CV-RMSE, and R2 of the model are 1296.41 W, 24.65% and 0.90, respectively. The proposed model can be used as the evaluation tool of DR management for decision making.