Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier

Zhengwei Qu, Hongwen Li, Yunjing Wang, Jiaxi Zhang, Ahmed Abu-Siada and Yunxiao Yao
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Zhengwei Qu: Key Laboratory of Power Electronics for Energy Conservation and Drive Control, Yanshan University, Qinhuangdao 066004, China
Hongwen Li: Key Laboratory of Power Electronics for Energy Conservation and Drive Control, Yanshan University, Qinhuangdao 066004, China
Yunjing Wang: Key Laboratory of Power Electronics for Energy Conservation and Drive Control, Yanshan University, Qinhuangdao 066004, China
Jiaxi Zhang: Key Laboratory of Power Electronics for Energy Conservation and Drive Control, Yanshan University, Qinhuangdao 066004, China
Ahmed Abu-Siada: School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth WA 6102, Australia
Yunxiao Yao: State Grid Hubei DC Operation and Maintenance Company, Yichang 443008, China

Energies, 2020, vol. 13, issue 8, 1-20

Abstract: Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data on the users’ side. To improve the auditing efficiency of grid enterprises, a new electricity theft detection method based on improved synthetic minority oversampling technique (SMOTE) and improve random forest (RF) method is proposed in this paper. The data of normal and electricity theft users were classified as positive data (PD) and negative data (ND), respectively. In practice, the number of ND was far less than PD, which made the dataset composed of these two types of data become unbalanced. An improved SOMTE based on K-means clustering algorithm (K-SMOTE) was firstly presented to balance the dataset. The cluster center of ND was determined by K-means method. Then, the ND were interpolated by SMOTE on the basis of the cluster center to balance the entire data. Finally, the RF classifier was trained with the balanced dataset, and the optimal number of decision trees in RF was decided according to the convergence of out-of-bag data error (OOB error). Electricity theft behaviors on the user side were detected by the trained RF classifier.

Keywords: smart grid; nontechnical losses; electricity theft detection; synthetic minority oversampling technique; K-means cluster; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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