Enhancing Climate Resilience through Machine Learning in Renewable Energy

Abstract

Climate change poses significant challenges to energy systems, including the renewable energy sector, where variability and unpredictability in weather patterns affect energy generation and distribution. Machine learning (ML) offers transformative solutions to enhance climate resilience by optimizing renewable energy systems, predicting weather impacts, and improving resource allocation. This paper explores the intersection of ML and renewable energy, focusing on strategies to mitigate climate impacts. We examine how ML-based forecasting, resource optimization, and anomaly detection contribute to system reliability and sustainability. By integrating advanced algorithms into renewable energy operations, stakeholders can address climate uncertainties more effectively.