A Lightweight Load Identification Model Update Method Based on Channel Attention

With the development of smart grids and home energy management systems, accurate load identification has become an important part of improving energy efficiency and ensuring electrical safety. However, traditional load identification methods struggle with high computational overhead and long model update times, which hinder real-time performance. In this study, a load identification method based on the channel attention mechanism is proposed for the lightweight model update problem in the electrical load identification task. To overcome this challenge, we construct color V-I trajectory maps by extracting the voltage and current signals of electrical devices during steady-state operation, and combine the convolutional neural network and channel attention mechanism for feature extraction and classification. Experimental results show that the proposed method significantly improves the accuracy, precision, recall, and F1-score compared with traditional methods on the public dataset, and tests on real hardware platforms verify its efficiency and robustness. This suggests that the lightweight model update method based on the channel attention mechanism holds great promise for smart grid applications, particularly in real-time systems with limited computational resources.