AI-Powered Digital Twins for Optimizing Smart City Infrastructure and Energy Management

Abstract

This study explores the application of AI-powered digital twins in optimizing smart city infrastructure and energy management. By integrating real-time data from IoT-enabled systems with advanced machine learning models, the research demonstrates significant improvements in key urban systems, including energy demand forecasting, traffic optimization, and infrastructure resilience. The AI-driven digital twin models achieved up to a 30% reduction in traffic congestion, 15% peak demand reduction in energy grids, and enhanced system resilience during demand surge events by 40%. These results are supported by simulations and optimization models that reduced energy consumption, improved renewable energy integration, and minimized traffic congestion through predictive modeling. Additionally, the digital twin models optimized water distribution systems and enabled better management of critical urban assets, contributing to more efficient resource utilization. Expert feedback on these models highlighted their practical applicability, showcasing positive reception from urban planners and infrastructure managers. The findings underscore the transformative potential of AI and digital twins in creating more sustainable, resilient, and efficient urban environments. However, the study also identifies challenges such as data privacy concerns and scalability issues, which need to be addressed in future developments. Overall, this research contributes to the evolving field of smart cities, demonstrating how AI-powered digital twins can play a crucial role in urban management and sustainability.