Smart City Gnosys

Smart city article details

Title	Ai-Driven Optimization Of Renewable Energy Storage Systems In Smart Cities Using Improved Binary Genetic Algorithm
ID_Doc	7031
Authors	Yan Y.; Liang Y.
Year	2025
Published	Journal of Circuits, Systems and Computers, 34, 6
DOI	http://dx.doi.org/10.1142/S0218126625501373
Abstract	Today, the utilization and management of renewable energy have become integral to the development of smart cities. This paper explores the application of Artificial Intelligence (AI) in analyzing energy storage and renewable energy systems within smart city contexts. We introduce a joint optimization method that combines two-stage input feature selection and parameter training in traditional prediction models. This method integrates physical, statistical, and AI modeling techniques. Additionally, we conduct comprehensive experiments on feature input selection using a Binary Genetic Algorithm (BGA) and two-stage model parameter optimization based on a Natural Evolution Strategies (NES). The experimental results provide strong support for the conclusions of this study. Finally, we simulate the full charge process of energy storage in a smart city and predict the full charge data for batteries B0005, B0006, B0007, and B0029. The results indicate that the NES-BGA-ELM approach outperforms the BGA-ELM method in terms of error reduction. © 2025 World Scientific Publishing Company.
Author Keywords	BGA; energy storage; renewable energy; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
54129	0.871	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)
35996	0.867	Haque A.; Malik A.	Machine Learning In Renewable Energy Systems For Smart Cities	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things (2024)
50838	0.865	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)
31927	0.86	Liu F.; Li X.	Integrating Ai Deep Reinforcement Learning With Evolutionary Algorithms For Advanced Threat Detection In Smart City Energy Management	IEEE Access, 12 (2024)
35034	0.859	Camacho J.D.J.; Aguirre B.; Ponce P.; Anthony B.; Molina A.	Leveraging Artificial Intelligence To Bolster The Energy Sector In Smart Cities: A Literature Review	Energies, 17, 2 (2024)
10408	0.856	Singh A.J.; Gupta N.; Kumar S.; Sharma S.; Upadhyay S.; Kumar S.	Artificial Intelligence And Machine Learning Applications For Sustainable Development	Artificial Intelligence and Machine Learning Applications for Sustainable Development (2025)
29761	0.855	John A.; Mohan S.K.; Padmanaban S.; Hamid Y.	Hybrid Intelligent Approaches For Smart Energy: Practical Applications	Hybrid Intelligent Approaches for Smart Energy: Practical Applications (2022)
13413	0.855	Chen L.; Li X.; Zhu J.	Carbon Peak Control For Achieving Net-Zero Renewable-Based Smart Cities: Digital Twin Modeling And Simulation	Sustainable Energy Technologies and Assessments, 65 (2024)
35986	0.854	Ahmed S.R.; Hussain A.-S.T.; Majeed D.A.; Jghef Y.S.; Tawfeq J.F.; Taha T.A.; Sekhar R.; Solke N.; Ahmed O.K.	Machine Learning For Sustainable Power Systems: Aiot-Optimized Smart-Grid Inverter Systems With Solar Photovoltaics	Lecture Notes in Networks and Systems, 1036 LNNS (2024)
53000	0.852	Jin G.; Huang Z.	Statistical Pathways To Low-Carbon Cities: Analyzing Renewable Integration, Energy-Efficient Design, And Job Creation	Sustainable Cities and Society, 107 (2024)