| Abstract |
The pressing challenge of mitigating carbon emissions towards achieving net-zero energy in smart cities, especially in the context of renewable energy integration, necessitates renewable energy integration in the smart energy management. This research focuses on harnessing renewable energy sources to curb emissions using advanced machine learning of Gated Recurrent Unit (GRU). Leveraging the Ant Colony Optimization (ACO), an evolutionary algorithm, we tackled the complexities of smart city energy management and optimization. ACO, inspired by the collective behavior of ants, allowed us to navigate the intricate landscape of energy distribution, optimizing resources for maximum efficiency. Furthermore, this article develops Digital Twin technology as a practical solution for modeling and simulating smart city environments. By creating detailed digital replicas of urban systems, Digital Twin facilitated precise analysis and experimentation. Our study utilized these digital models as practical case studies, enabling us to simulate various scenarios and optimize energy usage effectively. The synergy of renewable energy integration, ACO-driven optimization, and Digital Twin modeling offers a holistic and intelligent approach to propel smart cities towards a sustainable, net-zero energy future. |
