Electric Theft Detection Based on Multilayer Backpropagation Neural Network Optimized by Sine Chaotic Genetic Algorithm

Abstract

Abstract In the era of big data, the growing volume of data in electrical systems has led to a rise in electric theft incidents, posing challenges to grid security. This paper introduces a detection method using the Sine chaotic genetic algorithm to optimize multilayer Backpropagation (BP) neural networks. Initially, a comprehensive dataset is compiled through extensive data collection. A multilayer BP neural network is then trained on this dataset for automated theft identification. Leveraging the Sine chaotic genetic algorithm further enhances network performance. Experimental results show an 88% prediction accuracy, offering improved accuracy, speed, and usability over traditional methods.