Artificial Intelligence for Energy Network Optimization and Climate Change Response

Abstract

Artificial intelligence offers transformative potential for optimizing global energy networks and enhancing climate change prediction and response, yet current approaches remain fragmented across sectors and scales. This paper proposes a unified AI framework integrating energy network optimization with climate change prediction and response, leveraging deep learning for renewable energy forecasting, reinforcement learning for grid optimization, and observation-informed neural networks for climate impact prediction. The framework achieves 94% accuracy in 24‑hour solar forecasting and 89% accuracy in 6‑hour wind forecasting. Reinforcement learning‑based optimization reduces transmission losses by 12% and increases renewable utilization by 18%. Observation‑informed deep learning reduces ENSO projection uncertainty by 54% under high‑emission scenarios. Integrated case studies demonstrate improved renewable integration and enhanced early warning for climate‑related disasters. A unified AI framework can simultaneously optimize energy networks and enhance climate resilience, though successful implementation requires addressing challenges including data interoperability, model interpretability, and cross‑sectoral governance.