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A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods

Author

Listed:
  • Qinyu Huang

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

  • Zhenli Tang

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Xiaofeng Weng

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Min He

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Fang Liu

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Mingfa Yang

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

  • Tao Jin

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

Abstract

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method’s effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:275-:d:1313481
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    References listed on IDEAS

    as
    1. Farah Mohammad & Kashif Saleem & Jalal Al-Muhtadi, 2023. "Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    2. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
    3. Liu, Yulong & Jin, Tao & Mohamed, Mohamed A., 2023. "A novel dual-attention optimization model for points classification of power quality disturbances," Applied Energy, Elsevier, vol. 339(C).
    4. 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.
    5. 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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