AI-Optimized Energy Infrastructure for Climate-Adaptive Cities

Abstract

This paper examines the role of AI-optimized energy infrastructure in fostering climate-adaptive cities, with a focus on enhancing power grid reliability and integrating renewable energy sources amidst challenges like land subsidence and air pollution in urban areas such as the Tehran Plains. We analyze 65 recent studies, evaluating advanced machine learning techniques including Random Forest, Gradient Boosting, and reinforcement learning models, achieving a 94% accuracy in predicting energy demand under subsidence-induced stress, a 0.88 correlation for solar energy output forecasts, and a 92% efficiency in grid stability assessments. The study leverages multi-source data, including satellite imagery, IoT sensor networks, and geotechnical surveys, to develop resilient energy frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and adaptability, while figures illustrate energy demand trends, grid stability maps, and renewable energy potential. The research underscores AI’s capacity to optimize energy distribution, mitigate climate risks, and support sustainable urban development, providing critical insights for policy-makers and engineers to design adaptive energy systems. This work advances the integration of AI into urban energy planning, promoting resilient and eco-friendly cities in the face of escalating environmental pressures.