Dynamic Generative Residual Graph Convolutional Neural Networks for Electricity Theft Detection

Electricity theft is a critical issue in modern power systems, leading to financial losses and reduced reliability of electricity distribution networks. The growing use of Advanced Metering Infrastructure (AMI) has enabled smart meters to collect detailed consumption data, offering new opportunities to detect abnormal usage patterns. However, many existing detection methods rely only on individual load curves and fail to capture temporal dependencies, periodic behaviors, and hidden correlations within the data. To overcome these limitations, this study proposes a Dynamic Generative Residual Graph Convolutional Neural Network (DG RGCN). The model constructs dynamic graphs by updating adjacency matrices during training, allowing it to represent evolving relationships among electricity users. To address the challenge of imbalanced datasets, SMOTE oversampling and class weight adjustment are applied, ensuring improved detection accuracy. The proposed approach is evaluated on the SGCC smart meter dataset, and experimental results demonstrate that DG RGCN significantly outperforms traditional machine learning and deep learning models. By combining dynamic graph learning with residual connections, the model provides a practical and effective solution for electricity theft detection, enhancing both security and efficiency in smart grid systems.