Application of Grey Prediction in a GA-BP Power Theft Algorithm

Statistics show that power theft is one of the main reasons for the dramatic increase in power grid line loss. In this paper, a genetic algorithm is used to optimize a neural network and establish a power theft prediction model. With the grey prediction model, the predicted values of variables are obtained and then applied to the prediction model of a GA-BP neural network to obtain relatively accurate predictions from limited samples, reducing the absolute error. Through the two levels of prediction and analysis, the model is demonstrated to have good universality in predicting power theft behavior, and is a practical and effective method for power companies to carry out power theft analysis.