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A Novel Electricity Theft Detection Scheme Based on Text Convolutional Neural Networks

Author

Listed:
  • Xiaofeng Feng

    (Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Hengyu Hui

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Ziyang Liang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Wenchong Guo

    (Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Huakun Que

    (Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Haoyang Feng

    (Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Yu Yao

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Chengjin Ye

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yi Ding

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily or weekly loads, which is relatively inadequate to extract features from hours or more of fine-grained temporal data. In view of the above deficiencies, we propose a novel electricity theft detection scheme based on text convolutional neural networks (TextCNN). Specifically, we convert electricity consumption measurements over a horizon of interest into a two-dimensional time-series containing the intraday electricity features. Based on the data structure, the proposed method can accurately capture various periodical features of electricity consumption. Moreover, a data augmentation method is proposed to cope with the imbalance of electricity theft data. Extensive experimental results based on realistic Chinese and Irish datasets indicate that the proposed model achieves a better performance compared with other existing methods.

Suggested Citation

  • Xiaofeng Feng & Hengyu Hui & Ziyang Liang & Wenchong Guo & Huakun Que & Haoyang Feng & Yu Yao & Chengjin Ye & Yi Ding, 2020. "A Novel Electricity Theft Detection Scheme Based on Text Convolutional Neural Networks," Energies, MDPI, vol. 13(21), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5758-:d:439421
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    References listed on IDEAS

    as
    1. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft," Energy Policy, Elsevier, vol. 39(2), pages 1007-1015, February.
    2. Kumar V., Sampath & Prasad, Jagdish & Samikannu, Ravi, 2017. "Overview, issues and prevention of energy theft in smart grids and virtual power plants in Indian context," Energy Policy, Elsevier, vol. 110(C), pages 365-374.
    3. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
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    Cited by:

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    4. Rui Xia & Yunpeng Gao & Yanqing Zhu & Dexi Gu & Jiangzhao Wang, 2022. "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, MDPI, vol. 15(19), pages 1-25, October.

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