Research on building energy consumption prediction based on multi-level attention mechanism

Abstract

According to the multi-dimensional and multi feature characteristics of building energy consumption data, considering the fluctuation of long-time series prediction, partial attention mechanism, full convolution neural network and full-time attention mechanism are introduced to capture the potential correlation between features and targets and the weight between features, and an improved GeoMAN (Geo-sensory Multi-level Attention Networks) building energy consumption prediction model is proposed, Realize the prediction of building energy consumption data. The improved GeoMAN model is used to compare the prediction indexes with four groups of common models, and other data sets are input to verify the generalization ability of the model. The experimental results show that the model performs better in RMSE, Mae and MRE, reflecting better predictability and robustness.