Amidst the accelerating global shift towards sustainable energy, there has been a pronounced upsurge in the assimilation of renewable energy technologies (RET) within conventional power infrastructure. This shift aims to make these systems more efficient, reliable, and affordable. Artificial intelligence (AI) plays a vital role in this change. This review examines current research on how AI and renewable energy are connected, highlighting key methods, challenges, and achievements. It encompasses a wide array of AI-driven applications aimed at optimizing diverse operational dimensions of RET, such as spatiotemporal resource evaluation, energy yield forecasting, real-time system surveillance, intelligent control architectures, and seamless grid interfacing. Advanced computing techniques like machine learning, artificial neural networks, and optimization algorithms are explored for their ability to manage large and complex data sets, thereby augmenting predictive precision and enabling adaptive system behavior. This improves prediction accuracy and allows systems to adjust as needed. Some challenges in AI use for RET include unpredictable data, less transparency in models, and limits on real-time responses. Addressing these issues can significantly boost energy production, cut costs, and improve grid stability. The review also looks at potential future advancements poised to redefine the field.
Aamir Sohail (2026) studied this question.