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Review on Interpretable Machine Learning in Smart Grid

Author

Listed:
  • Chongchong Xu

    (School of Automation, Central South University, Changsha 410083, China)

  • Zhicheng Liao

    (School of Automation, Central South University, Changsha 410083, China)

  • Chaojie Li

    (Department of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW 2052, Australia)

  • Xiaojun Zhou

    (School of Automation, Central South University, Changsha 410083, China)

  • Renyou Xie

    (Department of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW 2052, Australia)

Abstract

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

Suggested Citation

  • Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4427-:d:841503
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    References listed on IDEAS

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    Cited by:

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    6. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.

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