Data-driven intelligent method for detection of electricity theft

Anti-electricity leakage or stealing plays a crucial role in the energy market as accurate detection is beneficial for electricity safety, unit commitment, reduction of corporate losses, and so on. In a contemporary competitive energy market, it can significantly improve the efficiency of electric power enterprises and reduce the opera-tional cost of the power system. In this study, through collecting historical data and analyzing the characteristics of electricity consumption, we established the index system for the detection of electricity leakage or stealing. Based on this, a focal loss-based 1d densely connected convolutional network, which we term the FLB-DCNNs, is proposed for the detection of electricity leakage or stealing. The electricity theft samples obtained in the actual on-site inspection are researched in the empirical analysis, and the results prove the effectiveness of the proposed approach. It attains an average precision of 98.51% for detecting electricity leakage or stealing users, and the average recall rate also reaches 98.17%. The recommended procedure provides a new idea for electricity theft detection and is easily transplanted to other related fields. Our data and code are available at https://github. com/xtu502/electricity-theft-detection.