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Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network

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Listed:
  • Rongheng Lin

    (State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Shuo Chen

    (State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Zheyu He

    (State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Budan Wu

    (State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Hua Zou

    (State Key Laboratory of Networking and Switching Technology, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Xin Zhao

    (Economic & Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

  • Qiushuang Li

    (Economic & Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

Abstract

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.

Suggested Citation

  • Rongheng Lin & Shuo Chen & Zheyu He & Budan Wu & Hua Zou & Xin Zhao & Qiushuang Li, 2024. "Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network," Energies, MDPI, vol. 17(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3904-:d:1451854
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    References listed on IDEAS

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