AI-Enhanced Digital Twins for Energy Efficiency and Carbon Footprint Reduction in Smart City Infrastructure
Yankun Li
Abstract
The combination of Artificial Intelligence (AI) and Digital Twin technology provides disruptive potential to increase energy efficiency and carbon footprint in smart city infrastructure. Digital Twins — virtual copies of the real-world systems — are augmented by AI algorithms that enable continuous monitoring, predictive analysis and optimization. In this paper, we explore the use of AI-based Digital Twins on smart buildings, transport networks and smart grids to save significant amounts of energy and drive sustainability. This is done through machine learning and reinforcement learning algorithms which identify patterns of energy use with high precision and helps to reduce the energy usage in smart buildings by 25-30%. For transportation, AI-enabled traffic infrastructure reduced carbon emissions by 20% and enhanced EV infrastructure efficiency by 18%. The smart grids were better served by predictive energy distribution, which allowed for a 15% decrease in losses and a 20% rise in the use of renewable energy. All of these results point towards the potential of AI-augmented Digital Twins to reshape city planning, optimise resource consumption and play a key role in achieving global sustainability targets. This research underscores the need to embrace high-tech solutions for the next smart city projects to combat climate change and promote sustainable development.