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A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data

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Listed:
  • Minsung Jung

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Hyeonseok Jang

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Woohyeon Kwon

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Jiyun Seo

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Suna Park

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Beomdo Park

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Junseong Park

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Donggeon Yu

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

  • Sangkeum Lee

    (Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea)

Abstract

This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure

Suggested Citation

  • Minsung Jung & Hyeonseok Jang & Woohyeon Kwon & Jiyun Seo & Suna Park & Beomdo Park & Junseong Park & Donggeon Yu & Sangkeum Lee, 2025. "A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data," Energies, MDPI, vol. 18(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3720-:d:1701330
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    References listed on IDEAS

    as
    1. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    2. Jiyong Park & Taeyoung Jin & Sungin Lee & Jongroul Woo, 2021. "Industrial Electrification and Efficiency: Decomposition Evidence from the Korean Industrial Sector," Energies, MDPI, vol. 14(16), pages 1-18, August.
    3. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
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