Timing shift-based bi-residual network model for the detection of electricity stealing

Abstract

With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift-based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small scaled and randomly coherent with time. Besides, its detection accuracy is superior to the benchmark schemes, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), combined convolutional neural network and LSTM (CNN-LSTM) and Bi-ResNet.

Electricity theft detection algorithm based on contrast learning and cluster combination discrimination

“…Detection of electricity theft is very important to maintain the smooth operation of the power grid and ensure the economic benefits of power supply enterprises. With the steady development of smart grid system, the data volume of power load is increasing, which provides the basis for data-driven electricity theft detection method [2].…”