The Solar <scp>AI</scp> Nexus: Reinforcement Learning Shaping the Future of Energy Management

ABSTRACT The growing complexity of solar energy systems—driven by intermittency, variability, and the need for real‐time decision‐making—demands adaptive, intelligent control mechanisms beyond traditional forecasting and optimization methods. Reinforcement Learning (RL) has emerged as a powerful approach capable of addressing these challenges through sequential decision‐making and dynamic policy adaptation. This review critically examines the evolving role of RL in solar energy forecasting, energy storage management, and grid optimization. A wide spectrum of RL models is explored, including Deep Q‐Networks (DQN), Proximal Policy Optimization (PPO), and advanced Meta‐Reinforcement Learning (Meta‐RL) frameworks. These models demonstrate remarkable improvements in prediction accuracy, ramp smoothing, operational cost reduction, and system resilience across diverse energy scenarios. Hybrid approaches, combining RL with deep learning (DL) and ensemble techniques, further enhance scalability, adaptability, and control under uncertainty. Despite these advancements, limitations persist in computational efficiency, generalization across environments, and data pipeline robustness. By synthesizing methodological innovations, real‐world applications, and critical challenges, this review positions RL as a transformative framework for developing intelligent, sustainable, and resilient solar energy systems. This article is categorized under: Sustainable Energy &gt; Solar Energy Energy and Power Systems &gt; Energy Management