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Cyberattack-resilient load forecasting with adaptive robust regression

Author

Listed:
  • Jiao, Jieying
  • Tang, Zefan
  • Zhang, Peng
  • Yue, Meng
  • Yan, Jun

Abstract

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.

Suggested Citation

  • Jiao, Jieying & Tang, Zefan & Zhang, Peng & Yue, Meng & Yan, Jun, 2022. "Cyberattack-resilient load forecasting with adaptive robust regression," International Journal of Forecasting, Elsevier, vol. 38(3), pages 910-919.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:3:p:910-919
    DOI: 10.1016/j.ijforecast.2021.06.009
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    References listed on IDEAS

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    Cited by:

    1. He, Yang & Luo, Jian & Zheng, Yukai, 2025. "A novel ensemble support vector regression for load forecasting under data attacks," Energy, Elsevier, vol. 333(C).
    2. Pei Zhao & Jie Zhang & Guang Ling, 2024. "Load Probability Density Forecasting Under FDI Attacks Based on Double-Layer LSTM Quantile Regression," Energies, MDPI, vol. 17(24), pages 1-18, December.
    3. VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.

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