Electricity Theft Detection Based on ReliefF Feature Selection Algorithm and BP Neural Network

Abstract

As China’s distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model’s input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection.