Leveraging Machine Learning in Next-Generation Climate Change Adaptation Efforts by Increasing Renewable Energy Integration and Efficiency

This article examines the growing role of machine learning (ML) in promoting next-generation climate change adaptation through the improved integration and performance of renewable energy systems. As climate change accelerates, innovative solutions are urgently needed to enhance the resilience and sustainability of energy infrastructure.ML offers powerful capabilities to handle complex data sets, forecast energy supply and demand, and optimize grid operations. This review highlights key applications of ML, such as predictive maintenance, intelligent grid management, and the real-time optimization of renewable energy resources. It also examines current challenges, including data availability, model transparency, and the need for interdisciplinary collaboration, both in technology development and policy and regulation. By synthesizing recent research and case studies, thisarticle shows how ML can significantly improve the performance, reliability, and scalability of renewable energy systems. This review emphasizes the importance of aligning technological advances with policy and infrastructure development. Successful implementation requires not only ensuring technological capabilities (robust infrastructure, structured data sets, and interdisciplinary collaboration) but also the careful consideration and alignment of ethical and regulatory factors from strategic to regional and local levels. Machine learning is becoming a key enabler for the transition to more adaptive, efficient, and low-carbon energy systems in response to climate change.