Harnessing machine learning for sustainable futures: advancements in renewable energy and climate change mitigation

Abstract

Background Renewable energy and climate change are vital aspects of humanity. Energy is needed to sustain life on Earth. The exploration and utilisation of traditional fossil-based energy has led to global warming. The exploration and use of fossil-based energy have significantly contributed to global warming, making the shift to renewable energy crucial for mitigating climate change. Renewable energies offer a sustainable alternative that does not harm the environment. This review aims to examine the role of machine learning (ML) in optimising renewable energy systems and enhancing climate change mitigation strategies, addressing both opportunities and challenges in this evolving field. The vital significance of renewable energy and measures to circumvent climate change in modern civilisation is first contextualised in the review. It draws attention to the difficulties encountered in these fields and describes the exciting potential of ML to solve them. Important discoveries highlight how ML can improve renewable energy technology scalability, dependability and efficiency while enabling more precise climate change projections and practical mitigation strategies. Simultaneously, issues including ethical considerations, interpretability of models and data quality demand attention. Method This review conducted a systematic literature analysis on the application of ML in renewable energy and climate change mitigation. It involved a comprehensive search, selection, and analysis of recent studies, focusing on ML’s role in energy forecasting, predictive maintenance, and climate modelling. The review synthesised key developments, challenges, and future directions, emphasising the need for ongoing transdisciplinary research to fully realise ML’s potential in advancing sustainable energy solutions. Result The review found that machine learning significantly enhances renewable energy system efficiency, scalability, and climate change mitigation through improved forecasting, predictive maintenance, and climate modelling. However, challenges like ethical concerns, model interpretability, and data quality persist. Ongoing research is essential to fully leverage ML’s potential in these areas. Short conclusion The paper highlights how machine learning can be used to revolutionise the energy and climate change mitigation industries for sustainable futures. It promotes ongoing transdisciplinary research and innovation to fully realise ML’s synergistic potential and tackle urgent global issues. In the end, the review advances our knowledge of how to use ML to hasten the transition to a future that is more robust and sustainable.