Electric theft detection based on multi-source heterogeneous time series GRU network

Abstract

Abstract Electricity theft has long been one of the major problems faced by power supply enterprises. To improve the robustness and accuracy of power theft detection, this article explores the method of multi-source heterogeneous time series feature fusion and designs a gated cyclic unit network model that adapts to its feature fusion. Firstly, through correlation analysis, it is verified that there is a logical correlation between different time series features and classification features, providing a theoretical basis for feature fusion. Then, an encoder decoder model framework is constructed with an attention mechanism to achieve effective fusion and state detection of user multi-source time series features. The experimental results show that compared to a single data source, the fusion of multi-source features can significantly improve detection performance, and the designed model is superior to the control model. This study provides a reference for constructing an efficient power theft detection system and also provides examples of multi-source heterogeneous feature fusion in related fields.