A Lightweight Load Identification Model Update Method Based on Channel Attention

Yong Gao, Junwei Zhang, Mian Wang, Zhukui Tan and Minhang Liang ()
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Yong Gao: Electric Power Research Institute Guizhou Power Grid Co., Ltd., Guiyang 550000, China
Junwei Zhang: Electric Power Research Institute Guizhou Power Grid Co., Ltd., Guiyang 550000, China
Mian Wang: Electric Power Research Institute Guizhou Power Grid Co., Ltd., Guiyang 550000, China
Zhukui Tan: Electric Power Research Institute Guizhou Power Grid Co., Ltd., Guiyang 550000, China
Minhang Liang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China

Energies, 2025, vol. 18, issue 11, 1-20

Abstract: 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.

Keywords: load identification; lightweight model update; channel attention mechanism; small-scale hardware experimental platforms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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