Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (5): 193-204.doi: 10.3969/j.issn.1000-5641.2023.05.016

• Data Analytics • Previous Articles    

Identifying electricity theft based on residual network and depthwise separable convolution enhanced self attention

Zhishang DUAN1(), Yi RAN1, Duliang LYU1, Jie QI2, Jiachen ZHONG2, Peisen YUAN2,*()   

  1. 1. The Marketing Service Center (Capital Pooling Centers, Metering Centers), State Grid Xinjiang Electric Power Co. Ltd., Urumqi, 830000, China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
  • Received:2023-07-05 Online:2023-09-25 Published:2023-09-15
  • Contact: Peisen YUAN;


Power theft seriously endangers power equipment and personal safety, and causes significant economic losses for energy suppliers. Hence, it is important for these suppliers to accurately identify instances of power theft to reduce losses and increase efficiency. In this paper, based on the residual network (ResNet) structure, a 2D convolutional neural network is combined with a depthwise separable convolution enhanced self-attentive (DSCAttention) mechanism to improve the number of correctly-classified electricity theft users. In addition, electricity theft data often contains missing values, outliers, and positive and negative sample imbalance. Each of the above problems are treated separately using the zero-completion method, quantile transformation, and hierarchical splitting method, respectively. The proposed model has been extensively tested using real power theft data sets. The results show that the area under curve (AUC) index of the proposed model reaches a value of 91.92%, while mean average precision values MAP@100 and MAP@200 are measured reaching 98.58% and 96.77%, respectively. Compared with other electricity theft classification models, the proposed model performs the electricity theft classification task better. The method in this paper can be extended to electricity theft intelligent identification.

Key words: residual network, convolution enhancement, self-attention mechanism, depthwise separable convolution, identifying electricity theft

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