AI for a Greener Planet: Revolutionizing Energy Systems for Climate Resilience

The global energy sector is at a tipping point, driven by the dual imperatives of climate change mitigation and the transition to sustainable energy. This paper introduces a comprehensive AI-driven framework that revolutionizes modern energy systems through climate-aware forecasting, real-time control, adaptive demand response, and distributed optimization. By combining deep learning, reinforcement learning, and edge intelligence, the proposed architecture significantly enhances resilience, efficiency, and decarbonization. Real-world inspired case studies—including LSTM-based solar prediction, MPC-RL hybrid grid regulation, DRL-based demand flexibility, and AI-enabled microgrid resilience—demonstrate up to 41% forecast improvement, 50% faster frequency recovery, and 36% emissions reduction. The paper concludes with a policy roadmap to scale AI across infrastructure, governance, and global collaboration, establishing AI as a cornerstone in climate-resilient energy futures.