Electricity Theft Detection based on Iterative Interpolation and Fusion Convolutional Neural Network

Abstract

Electricity theft detection is important for discovering electricity theft users and reducing electricity loss. To address the problems that existing methods are inadequate for pre-processing electricity consumption data and capturing electricity consumption information of different periodic patterns, an electricity theft detection method based on iterative interpolation and fusion convolutional neural network (CNN) is proposed in this paper. In the proposed method, a Bayesian ridge regression-based data interpolation method is used to predict and supplement the missing values in the electricity consumption dataset from existing data, and a fusion CNN architecture including two convolutional branches is proposed to capture the characteristics of data with different periodic patterns. The experimental results showed that the proposed method outperforms the comparison methods in terms of accuracy, precision, recall, and other evaluation metrics, and can effectively recognize electricity theft users.