Review on Interpretable Machine Learning in Smart Grid

Published in

Energies(2022)

External Links:

TL;DR

The definition, motivations, properties, and classification of interpretability are elaborate and the future research directions of interpretable machine learning in the smart grid are discussed.

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.

Authors

Chongchong Xu

1 papers

Zhicheng Liao

1 papers

Chaojie Li

1 papers

References157 items

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Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Interpretable Machine Learning in Smart Grid

Published in

Energies(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

The definition, motivations, properties, and classification of interpretability are elaborate and the future research directions of interpretable machine learning in the smart grid are discussed.

Abstract

Authors

Chongchong Xu

1 papers

Zhicheng Liao

1 papers

Chaojie Li

1 papers

References157 items

Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI—Explainable Artificial Intelligence

Interpretable Memristive LSTM Network Design for Probabilistic Residential Load Forecasting

Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems

Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

Review on Deep Learning Research and Applications in Wind and Wave Energy

Xiaojun Zhou

1 papers

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Survey of clustering algorithms

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