A Nonintrusive Load Identification Method Based on Dual-Branch Attention GRU Fusion Network
Jie Yuan, Ran Jin, Lidong Wang, T. J. Wang
Abstract
Nonintrusive load identification can infer the operating status of various electrical appliances in a user’s household based on the electrical parameters collected by the overall household electricity meter. It helps users utilize electrical energy more scientifically and rationally. Due to significant differences in the working principles of appliances and their energy consumption, the effectiveness of load identification has not yet met practical application requirements. This article proposes a load identification method based on a dual-branch attention gated recurrent unit (GRU) fusion network structure, and the network integrates GRU, temporal convolution network (TCN), and attention mechanisms. It overcomes the limitation of GRU networks in extracting ultra-long-term temporal dependencies and compensates for TCN’s less effective exploration of medium and short-term temporal dependencies compared to GRU. As a result, it achieves better identification performance. Experiments on public datasets indicate that the proposed method achieves better load state identification performance compared to other methods.