Methods of Explainable Artificial Intelligence, Trustworthy Artificial Intelligence and Interpretable Machine Learning in Renewable Energy

摘要: In recent years, there has been a significant increase in the use of renewable energy (RE) resources in order to produce cleaner energy. The impact of decisions made by artificial intelligence models on energy efficiency is very important in the transition to these resources. With eXplainable Artificial Intelligence (XAI), various methods have been developed to increase trust, transparency, and decision-making by artificial intelligence models, but more research is needed in this area to enhance confidence in the performance, evaluation, and explanations of these models. The aim of this study is to investigate and analyze how RE systems can benefit from the use of XAI and Trustworthy AI and Interpretable AI, along with considering some criticisms. The results of the study suggest that XAI is a relatively new topic in the field of RE and requires more attention in order to be effectively applied in critical systems to improve trust and transparency.