AI for Solar Energy Systems
Abstract
The rapid expansion of solar energy has accelerated the adoption of Artificial Intelligence (AI) to improve forecasting accuracy, photovoltaic performance, intelligent control, fault diagnosis, energy storage management, and smart grid operation. Recent advances in machine learning, deep learning, Digital Twins, Physics-Informed Machine Learning, Edge AI, Federated Learning, and Generative AI have transformed solar energy systems into more adaptive and data-driven infrastructures. Despite these advances, challenges related to data quality, model generalization, computational efficiency, cybersecurity, and explainability continue to limit large-scale deployment. An integrated perspective is presented by connecting AI applications across photovoltaic systems, solar thermal technologies, energy storage, and intelligent energy networks within a unified renewable energy ecosystem. Emerging research trends and future directions highlight the evolution toward AI-native energy systems that support efficient, resilient, and sustainable intelligent societies.