Non-intrusive Load Decomposition for Smart Buildings Based on Parallel Connectivity Networks and Attention Mechanism

Abstract

To obtain the operating status of equipment and loads in buildings without installation of intrusive monitoring devices, non-intrusive load decomposition and monitoring methods have become the research focus of many scholars and researchers. To improve the accuracy of non-intrusive load decomposition, a non-intrusive load decomposition based on parallel connection network and attention mechanism is proposed. This proposed method educes the depth of network by ‘parallel connection’ and reduces the risk of overfitting. In additional, the dilated residual convolutional neural network and the bidirectional long short-term memory network are connected in parallel to extract features respectively, which greatly improves the representation ability of features. The attention mechanism is introduced to eliminate redundant information, focus on important information, and improve the decomposition performance. Finally, the domestic self-assessment data set is used, and different evaluation indicators are used for evaluation and comparison with other commonly used models. The simulation results show that the decomposition accuracy of proposed method is significantly improved.