Integrating AI Deep Reinforcement Learning With Evolutionary Algorithms for Advanced Threat Detection in Smart City Energy Management

The integration of Deep Reinforcement Learning (DRL) with Evolutionary Algorithms (EAs) represents a significant advancement in optimizing smart city energy operations, addressing the inherent uncertainties and dynamic conditions of urban environments. This study explores how the synergy between DRL and EAs, including Genetic Algorithms (GAs) and Differential Evolution (DE), can enhance the efficiency and sustainability of smart city energy systems. DRL, known for its adaptive learning capabilities in complex environments, is combined with EAs, which excel in exploring diverse solution spaces and managing multi-objective optimization problems. The proposed methodology leverages DRL’s ability to learn optimal policies through interaction with the environment and EAs’ robust search mechanisms to address stochastic elements in energy consumption and generation. This integration is applied to various components of smart city energy operations, such as demand response, energy storage management, and renewable energy integration. The results from simulated smart city environments demonstrate significant improvements in energy efficiency, cost reduction, and emission control. This study highlights the potential of combining DRL with EAs to provide a comprehensive approach to tackling the challenges of stochastic optimization, offering a promising solution for achieving adaptive and resilient urban energy management in the face of uncertainty. Application of this integrated approach to demand response, energy storage management, and renewable energy integration in simulated smart city environments resulted in a 15% improvement in energy efficiency, a 12% reduction in operational costs, and a 20% decrease in emissions. These numeric results underscore the effectiveness of combining DRL with EAs in achieving significant gains in energy management. The study highlights the potential of this integrated approach for addressing the challenges of stochastic optimization, offering a promising solution for adaptive and resilient urban energy systems in the face of uncertainty. This research paves the way for more sustainable and efficient smart city initiatives. The findings underscore the importance of this integrated approach for advancing smart city initiatives and fostering sustainable urban energy systems.