Explanatory Optimization of the Prediction Model for Building Energy Consumption

Traditional prediction models, which are based on artificial neural networks (ANNs), consider the various factors affecting building energy consumption comprehensively. However, their explanatory power is not ideal in actual application, resulting in prediction errors of building energy consumption. Thus, this paper pursues the explanatory optimization of the prediction model for building energy consumption. First, the authors displayed the architecture of the prediction model for building energy consumption, which is based on the temporal pattern attention mechanism (TPAM), and explained the principle of predicting building energy consumption. Then, the input of the TPAM was illustrated, and the execution steps of the model were depicted. Based on feature importance and the Shapley additive explanations (SHAP) method, the explanatory power of the proposed prediction model was analyzed, from the perspective of the time series features of building energy consumption prediction. The proposed model was proved effective through experiments.