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

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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)

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.

Suggested Citation

  • Zhengwei Qu & Hongwen Li & Yunjing Wang & Jiaxi Zhang & Ahmed Abu-Siada & Yunxiao Yao, 2020. "Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier," Energies, MDPI, vol. 13(8), pages 1-20, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2039-:d:347657
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    References listed on IDEAS

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    1. Bernat Coma-Puig & Josep Carmona, 2019. "Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection," Energies, MDPI, vol. 12(9), pages 1-17, May.
    2. Xiaoquan Lu & Yu Zhou & Zhongdong Wang & Yongxian Yi & Longji Feng & Fei Wang, 2019. "Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid," Energies, MDPI, vol. 12(18), pages 1-18, September.
    3. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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    Cited by:

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    3. Pamir & Nadeem Javaid & Saher Javaid & Muhammad Asif & Muhammad Umar Javed & Adamu Sani Yahaya & Sheraz Aslam, 2022. "Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit," Energies, MDPI, vol. 15(8), pages 1-20, April.
    4. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    5. Benish Kabir & Umar Qasim & Nadeem Javaid & Abdulaziz Aldegheishem & Nabil Alrajeh & Emad A. Mohammed, 2022. "Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks," Sustainability, MDPI, vol. 14(22), pages 1-19, November.

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