Machine learning for renewable energy advancements: Prospects and emerging techniques

The paradigm of machine learning (ML) in renewable energy systems offers disruptive prospects. For energy generation, management, and storage. The purpose of this review is to assess the impact of machine learning on the development of renewable energy systems, specifically in terms of enhancing their operational stability, forecasting, and optimization. Using supervised, unsupervised, and reinforcement learning techniques, a thorough analysis of the most recent research on the use of solar, wind, hydro, and bioenergy was conducted. According to the results, machine learning significantly improves prediction accuracy, grid stability, and energy storage performance when compared to classical methods. Deep…