Short-term thermal parameter prediction of building energy system based on MIE-JANET method

Abstract

The precise prediction of thermal parameters in building energy systems is foundational for the efficient and stable operation of the system, aiding in the formulation of optimal building operation strategies. Currently, in the field of architecture, there is a lack of parameter prediction for thermal parameters that involve multiple devices and thermal processes. Moreover, the widely used long short-term memory (LSTM) algorithm in this field tends to have a large number of network parameters, leading to longer training times. Aiming at these problems, a short-term predictive method for thermal parameters named MIE-JANET is proposed. Mutual information entropy was employed to select relevant features from system operation data. Multiple short-term prediction models were constructed by JANET mdoel. The results indicate the superior predictive performance compared to similar algorithms. When considering insufficient data, the mean absolute error for the JANET decreases by 4.9% relative to LSTM. With the same structure, the training time of the JANET model was reduced by more than 40% compared to LSTM. Moreover, The accuracy of the room temperature model, return water temperature model, and supply water temperature model has significantly improved compared to the physical model. This approach provides a foundational model for the intelligent control of building energy systems.