Optimization method of suspected electricity theft topic model based on chi-square test and logistic regression

Abstract

In recent years, the electricity theft has presented characteristics of high-tech and covert. Therefore, the factors that reflect the existence of stealing electricity become varied and complex. It brings the problems such as low efficiency and poor accuracy for power grid enterprises to identify the customers who had been stealing electricity. In this paper, Chi-square test and logistic regression are used to optimize the suspected electricity theft topic model. Chi-square test is used to determine the factors interrelated with the electricity theft firstly, and then the logistic regression algorithm is used to optimize the weights of the interrelated factors, and finally constructed a prediction function that can predict the customers who had been stealing electricity. Experiments show that the method proposed in this paper can help the power grid enterprises to identify the customers who had been stealing electricity, on account of having high accuracy rate, precision rate and recall rate.