Renewable energy management using explainable artificial intelligence

The world is facing ongoing efforts to rely more on Renewable energy sources (RES) to reduce carbon emissions and protect the environment. However, integrating RES can be challenging due to their variable nature. With the development of Artificial Intelligence (AI), RES management can be enhanced and improved to overcome limitations. However, AI is often viewed as a “black box” because it lacks explainability and transparency, particularly in sectors such as energy, which require reliability and trust in AI-driven decisions. Explainable Artificial Intelligence (XAI) has emerged as a vital research field within the RES management domain, addressing the need for understanding the reasoning behind AI-driven decisions. This review marks a new direction in this field, despite the limited number of studies available. Most studies have focused on solar and wind sources, as they are more widely available globally. The present study provides an overview of 48 studies reviewed. Presenting summaries of findings from the XAI technique regarding the most important feature of each management task, thereby improving performance and explainability. Also, this review presents a matrix of energy sources, tasks, and techniques that summarizes commonly adopted models and choices for explainability in the literature. This review identifies research gaps in existing literature and highlights the challenges and new trends.