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A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection

Author

Listed:
  • Yiran Wang

    (School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Shuowei Jin

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Ming Cheng

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

This paper proposes a novel convolution–non-convolution parallel deep network (CNCP)-based method for electricity theft detection. First, the load time series of normal residents and electricity thieves were analyzed and it was found that, compared with the load time series of electricity thieves, the normal residents’ load time series present more obvious periodicity in different time scales, e.g., weeks and years; second, the load times series were converted into 2D images according to the periodicity, and then electricity theft detection was considered as an image classification issue; third, a novel CNCP-based method was proposed in which two heterogeneous deep neural networks were used to capture the features of the load time series in different time scales, and the outputs were fused to obtain the detection result. Extensive experiments show that, compared with some state-of-the-art methods, the proposed method can greatly improve the performance of electricity theft detection.

Suggested Citation

  • Yiran Wang & Shuowei Jin & Ming Cheng, 2023. "A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10127-:d:1179995
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    References listed on IDEAS

    as
    1. Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    2. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    3. Simona-Vasilica Oprea & Adela Bâra & Florina Camelia Puican & Ioan Cosmin Radu, 2021. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    4. Sajad Ali & Min Yongzhi & Wajid Ali, 2023. "Prevention and Detection of Electricity Theft of Distribution Network," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    5. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    6. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Qinyu Huang & Zhenli Tang & Xiaofeng Weng & Min He & Fang Liu & Mingfa Yang & Tao Jin, 2024. "A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods," Energies, MDPI, vol. 17(2), pages 1-18, January.

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