Review on Interpretable Machine Learning in Smart Grid

Chongchong Xu, Zhicheng Liao, Chaojie Li, Xiaojun Zhou and Renyou Xie
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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

Energies, 2022, vol. 15, issue 12, 1-31

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.

Keywords: interpretable machine learning; explainable artificial intelligence; machine learning; deep learning; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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