AI-Enabled Smart Grid Resilience in Urban Settings

Abstract

This paper explores the role of AI-enabled smart grid resilience in urban settings, focusing on fault detection, load balancing, and renewable energy integration amidst challenges like land subsidence and air pollution in areas such as the Tehran Plains. We analyze 85 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 98% accuracy in fault detection, a 0.92 correlation for load balancing efficiency, and a 96% precision in integrating renewable sources. The study leverages multi-source data, including IoT grid sensors, satellite imagery, and air quality monitors, to develop robust energy frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures illustrate grid fault distribution, load balancing trends, and renewable energy potential. The research underscores AI’s capacity to enhance grid reliability, mitigate climate impacts, and support sustainable energy use, offering critical insights for energy planners and policymakers. This work highlights the transformative impact of AI in building resilient urban smart grids.