Abstract
This study focuses on how climate science and machine learning techniques may be used to improve power system resilience in the face of climate change. It emphasizes the significance of resilience and the principles of machine learning application in power systems. Predictive models for climate-related disruptions are among the most recent advances in merging climate research with machine learning. The review assesses the efficacy of various models in improving system resilience and their limitations and problems. Future research prospects, policy consequences, and recommendations for moving climate science and machine learning integration forward for power system resilience are highlighted. Overall, the need to integrate these technologies to address climate change concerns and improve power system resilience is emphasized in this analysis.
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