Machine Learning in Renewable Energy Forecasting: A Review of Developments, Challenges and Future Prospects

Accurate renewable energy forecasting is indispensable for addressing the inherent variability of sources such as solar and wind, both of which are pivotal to the global shift toward sustainable energy systems. Well-calibrated forecasts support grid stability, optimize energy distribution, and reduce unnecessary reliance on conventional power generation. Prevailing forecasting approaches exhibit well-documented limitations that constrain their effectiveness across heterogeneous regional conditions and evolving grid architectures. The research examines how machine learning technologies impact renewable energy development in the power industry. Supervised learning models such as ANNs and SVMs are explored for their predictive capabilities. Both PCA and K-Means clustering are evaluated for their effectiveness in handling feature extraction and trend analysis. Our research studies how Deep Q-Networks (DQN) and other reinforcement learning systems optimize energy management. Evidence consistently demonstrates that machine learning frameworks enhance predictive precision for energy trends and contribute to improved overall power grid performance. Nevertheless, substantial data demands and constrained scalability pathways continue to represent significant barriers to broader deployment. The findings of this review indicate that next-generation algorithms and robust data infrastructure are prerequisites for enabling efficient and reliable power distribution in modern grids. Bridging these gaps is essential to fulfilling long-term grid reliability objectives and advancing the broader trajectory toward sustainable energy infrastructure.