Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

Abstract

Electricity theft has been a major concern to the secure operation of power systems and the interests of power companies. Due to the different methods and types of electricity theft behaviors, it is difficult to determine the suspicion levels of consumers in the research of electricity theft detection. An electricity theft detection method based on stacked autoencoder (SAE) and the undersampling and re-sampling based random forest (UaRe-RF) algorithm is proposed in this work to formulate appropriate strategies for the practical electricity theft detection requirements of the power company. In the proposed method, the supervised SAE is first trained to extract electricity consumption features that are more adaptable to the classification algorithm for electricity theft detection. Then, the UaRe-RF algorithm is used to establish the class-balanced subsets and determine the suspicion level of each electricity theft user. Finally, two cases of different datasets of electricity consumers are studied for demonstrating the effectiveness of the proposed method, and the results show that higher classification accuracy and more targeted detection strategies can be achieved through the proposed method.

Keywords

• Electricity theft
• class imbalance
• suspicion level
• feature extraction
• machine learning
• random forest