Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities

Abstract

Despite widespread adoption and outstanding performance, machine learning models are considered as “black boxes”, since it is very difficult to understand how such models operate in practice. Therefore, in the power systems field, which requires a high level of accountability, it is hard for experts to trust and justify decisions and recommendations made by these models. Meanwhile, in the last couple of years, Explainable Artificial Intelligence (XAI) techniques have been developed to improve the explainability of machine learning models, such that their output can be better understood. In this light, it is the purpose of this paper to highlight the potential of using XAI for power system applications. We first present the common challenges of using XAI in such applications and then review and analyze the recent works on this topic, and the on-going trends in the research community. We hope that this paper will trigger fruitful discussions and encourage further research on this important emerging topic.

Keywords

• Power
• Energy
• Neural network
• Deep-learning
• Explainable artificial intelligence
• XAI