Harnessing Reinforcement Learning for Enhanced Solar Radiation Prediction: State-of-the-Art and Future Directions

Solar radiation prediction is critical for optimizing the performance of solar energy systems, yet traditional methods often need help to capture the complexity of this phenomenon. In recent years, reinforcement learning (RL) has emerged as a promising approach to address this challenge by enabling autonomous learning and decision-making in dynamic environments. This review paper presents a comprehensive overview of the state-of-the-art harnessing of RL techniques for enhancing solar radiation prediction. Our analysis is based on a systematic Scopus advanced search, which yielded 25 relevant documents published between 2018 and 2024. We categorize existing literature based on modeling strategies, input data sources, and evaluation methodologies, providing insights into key findings and approaches. We discuss the choice of RL algorithms, input feature selection, reward design, and model evaluation metrics. Additionally, we identify challenges such as data scarcity, interpretability issues, and generalization concerns and discuss potential solutions. Furthermore, we outline future research directions and emerging trends in the field, including transfer learning, domain adaptation, and multi-agent RL. This review aims to provide researchers, practitioners, and policymakers with a comprehensive understanding of the potential of RL for enhancing solar radiation prediction and to guide future research efforts towards more accurate and reliable renewable energy forecasting systems.