Building energy consumption prediction based on VMD-CNN-BILSTM-Attention

Abstract

Accurate building energy consumption prediction can help buildings optimize energy allocation and save energy. To achieve accurate and reliable building energy consumption prediction, this paper proposes a building energy consumption prediction method based on VMD decomposition and noise reduction, i.e., CNN-BILSTM-Attention hybrid prediction. In this paper, we first use the VMD method to decompose the historical energy consumption series to obtain relatively smooth multiple components, which lays the foundation for accurate prediction. Then we combine Convolutional Neural Network (CNN) and Bi-directional Long and Short Term Memory (BILSTM) network to fully extract the Spatio-temporal characteristics of energy consumption data and then introduce the Attention mechanism to automatically assign corresponding weights to the BILSTM hidden layer states to distinguish the importance of different influencing factors on energy consumption and improve the prediction accuracy. To verify the effectiveness of the model, the most later analyzed with real building energy consumption data of a region, and the prediction results were shown by python, and the prediction results were improved by 40% compared with the traditional machine learning algorithm.