Methods of Explainable Artificial Intelligence (XAI), Trustworthy Artificial Intelligence (TAI) and Interpretable Machine Learning (IML) in Renewable Energy

This is the authors' abstract. We don't add key points for this paper.

In recent years, tendency to renewable energy resources has increased considerably in order to obtain cleaner energy. The effect of the decisions taken by artificial intelligence models on energy efficiency is very important in the transformation of these resources. With eXplainable Artificial Intelligence (XAI), various methods have been developed for trust, transparency and decision making by artificial intelligence models, but more models need to be developed in this area in order for decision-making mechanisms to increase confidence in performance, evaluation and explanations. The aims of the study are to review and analyze how RE systems can benefit from XAI applications with some criticisms. The results have shown that XAI in a new topic in RE and requires more attentions to be applied in critical systems to improve the trust and transparency.