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 understanding and hybrid methods exhibit the largest and second-largest improvements, respectively, when compared to classical approaches. According to these outcomes, integrating cutting-edge machine learning technology is not only likely but also required to meet the global sustainability goals in the long run, achieve the goal of rapidly adopting renewable energy, and increase independence from fossil fuels. • In renewable energy systems, machine learning improves grid management, prediction, and optimization. • In forecasting and energy storage, deep learning and hybrid models perform better than conventional techniques. • Strong regulatory frameworks and interdisciplinary cooperation are necessary for the integration of ML with renewable energy systems. • For machine learning to be widely used in energy systems, issues like data quality and processing cost must be resolved.