AI-driven innovations in solar energy systems: advances, challenges, and emerging trends

Solar energy systems have emerged as a pivotal component of the global transition toward sustainable power, offering a robust alternative to fossil fuel systems. These systems contribute to energy security with low environmental, ecological, and social impacts. Recent advances in artificial intelligence (AI) have created new opportunities across many fields, including renewable energy systems. The synergistic integration of AI techniques in solar energy applications has accelerated system development and improved performance. This study reviews AI applications in solar energy systems over the last five years (2020–2025). The review covers recent AI-based approaches in power and energy forecasting, fault detection and maintenance, energy management, grid integration, and optimization, highlighting the emerging roles of Digital Twin (DT) and Explainable AI (XAI). Despite these advantages, AI deployment in solar systems confronts multifaceted technical, economic, environmental, and ethical challenges, including weather variability, high initial and operational costs, data security and privacy concerns, and potential job displacement. Addressing these challenges through responsible and innovative AI adoption can enhance the role of AI in advancing sustainable solar energy systems. Future directions include the transition toward Generative Artificial Intelligence (Gen-AI), Causal AI, and Foundation Models, alongside the shift from centralized control to autonomous and intelligent systems, which together can further unlock the potential of AI in the solar sector.