Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

Abstract

Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, the existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on convolutional and joint distribution adaptation (CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), marginal distribution adaptation (MDA), and conditional distribution adaptation (CDA). The convolutional component can efficiently extract the customer's electricity characteristics. The MDA can match marginal probability distributions and solve the discrepancies of residents from different regions, while CDA can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity-theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial (ISET) and State Grid Corporation of China (SGCC), and metrics, including ACC, recall, false positive rate (FPR), area under curve (AUC), and F1 score, are used for evaluation. Compared with other methods, including some machine learning methods, such as decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), some deep learning methods, such as recurrent neural network (RNN), convolutional neural network (CNN), and wide and deep CNN (WCNN), and some up-to-date methods, such as balanced distribution adaptation (BDA), weighted and BDA (WBDA), random convolutional kernel transform (ROCKET), and minimally ROCKET (MiniROCKET), our proposed method has a better effect on identifying electricity theft from different regions.