Smart City Gnosys
Smart city article details
| Title | Integrating Ai Deep Reinforcement Learning With Evolutionary Algorithms For Advanced Threat Detection In Smart City Energy Management |
|---|---|
| ID_Doc | 31927 |
| Authors | Liu F.; Li X. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3471076 |
| Abstract | 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. |
| Author Keywords | energy efficiency; evolutionary algorithms; operations; renewable energy integration; smart city energy; Stochastic optimization |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
35938  | 0.892 | Li M.; Mour N.; Smith L. | Machine Learning Based On Reinforcement Learning For Smart Grids: Predictive Analytics In Renewable Energy Management | Sustainable Cities and Society, 109 (2024) |
| 1392 | 0.889 | Yiğit Y.; Karabatak M. | A Deep Reinforcement Learning-Based Speed Optimization System To Reduce Fuel Consumption And Emissions For Smart Cities | Applied Sciences (Switzerland), 15, 3 (2025) |
| 40637 | 0.886 | Alatawi M.N. | Optimization Of Home Energy Management Systems In Smart Cities Using Bacterial Foraging Algorithm And Deep Reinforcement Learning For Enhanced Renewable Energy Integration | International Transactions on Electrical Energy Systems, 2024 (2024) |
| 7714 | 0.883 | Ludolfinger U.; Martens M. | An Autonomous Energy Management Concept For Sustainable Smart Cities | 2023 IEEE European Technology and Engineering Management Summit, E-TEMS 2023 - Conference Proceedings (2023) |
| 44356 | 0.879 | Faghri S.; Tahami H.; Amini R.; Katiraee H.; Godazi Langeroudi A.S.; Alinejad M.; Ghasempour Nejati M. | Real-Time Energy Flexibility Optimization Of Grid-Connected Smart Building Communities With Deep Reinforcement Learning | Sustainable Cities and Society, 119 (2025) |
| 10071 | 0.877 | Ullah Z.; Al-Turjman F.; Mostarda L.; Gagliardi R. | Applications Of Artificial Intelligence And Machine Learning In Smart Cities | Computer Communications, 154 (2020) |
| 38102 | 0.875 | Vazquez-Canteli J.; Detjeen T.; Henze G.; Kämpf J.; Nagy Z. | Multi-Agent Reinforcement Learning For Adaptive Demand Response In Smart Cities | Journal of Physics: Conference Series, 1343, 1 (2019) |
| 50838 | 0.874 | Mongaillard T.; Lasaulce S.; Varma V.S. | Smart Energy Management: From Conventional Optimization To Generative Ai Techniques | Lecture Notes in Intelligent Transportation and Infrastructure, Part F99 (2025) |
| 6943 | 0.873 | Cakir L.V.; Duran K.; Thomson C.; Broadbent M.; Canberk B. | Ai In Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model For Green Cities | IEEE International Conference on Communications (2024) |
| 54129 | 0.872 | Tungom C.E.; Niu B.; Wang H. | Swapp: Swarm Precision Policy Optimization With Dynamic Action Bound Adjustment For Energy Management In Smart Cities | Applied Energy, 377 (2025) |