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Power Consumption Anomaly Detection of Smart Grid Based on CAE-GRU

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Listed:
  • Jing Yang

    (Guizhou Power Grid Co., Ltd., Guiyang 550002, China
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Qiang Song

    (Guizhou Power Grid Co., Ltd., Guiyang 550002, China)

  • Lei Hu

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Minyong Xin

    (Guizhou Power Grid Co., Ltd., Guiyang 550002, China)

  • Renxin Xiao

    (Transport Engineering College, Kunming University of Technology, Kunming 650500, China)

Abstract

With the growth of global energy demand, the application of smart grid technology has become widespread. Anomaly detection in power systems is crucial for ensuring the stability and economy of power supply. Deep learning technologies offer new opportunities in this field. This paper proposes a deep learning approach based on Convolutional Autoencoders (CAEs) and Gated Recurrent Units (GRUs) for anomaly detection in smart grid power data. This method integrates three types of feature data, namely user power consumption, line loss correlation, and meter error, and combines the moving window technology to construct a CAE-GRU network model. Experimental results show that, compared with traditional methods, this method has higher accuracy in anomaly detection, which can effectively identify potential problems in the power grid and provide strong support for the optimized operation of the smart grid.

Suggested Citation

  • Jing Yang & Qiang Song & Lei Hu & Minyong Xin & Renxin Xiao, 2025. "Power Consumption Anomaly Detection of Smart Grid Based on CAE-GRU," Energies, MDPI, vol. 18(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4787-:d:1745160
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    References listed on IDEAS

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    1. Yousaf Murtaza Rind & Muhammad Haseeb Raza & Muhammad Zubair & Muhammad Qasim Mehmood & Yehia Massoud, 2023. "Smart Energy Meters for Smart Grids, an Internet of Things Perspective," Energies, MDPI, vol. 16(4), pages 1-35, February.
    2. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Taeseop Park & Keunju Song & Jaeik Jeong & Hongseok Kim, 2023. "Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants," Energies, MDPI, vol. 16(14), pages 1-20, July.
    4. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
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