Machine Learning in Renewable Energy: Strategies for Improved Performance and Integration

Abstract

As the world transitions toward sustainable energy solutions, optimizing renewable energy systems becomes increasingly crucial. Machine learning (ML) offers transformative potential to enhance the performance and integration of renewable energy technologies. This paper explores various ML strategies for improving renewable energy systems, focusing on predictive analytics, optimization algorithms, anomaly detection, and control systems. Through case studies and an examination of current challenges, this research provides insights into how ML can address the complexities of renewable energy systems and suggests future directions for research and development.