Smart City Gnosys

Smart city article details

Title	Expanding Electric Vehicle Adoption And Power Demand Forecasting For Sustainable Smart Cities: A Case Study Of South Korea
ID_Doc	25154
Authors	Lee A.; Jang H.; Yang E.; Liu X.; Park S.
Year	2024
Published	2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024
DOI	http://dx.doi.org/10.1109/APWCS61586.2024.10679279
Abstract	This study aims to establish power stability to expand the adoption of EV in smart cities. Unlike fossil fuel-based power generation, which can respond to real-time demand, predicting power demand is essential if the adoption of renewable energy, which struggles with real-time response, increases. This research was conducted based on South Korea's power consumption to transform it into a sustainable smart city based on renewable energy. Using past power data from South Korea and employing the Long Short-Term Memory (LSTM) model, we predict energy generation, consumption, and EV charging station energy usage from 2024 to 2030. The study results indicate that with the current policies, South Korea is likely to face power supply challenges by 2030 to achieve the targeted renewable energy generation rates, EV adoption rates, and annual GDP growth required for G7 invitation. To achieve carbon neutrality and expand EV adoption, it is crucial not only to meet the current renewable energy generation targets but also to build renewable energy infrastructure that can meet future demand. The prediction of energy demand and supply in this study suggests that if economic growth continues and EV adoption expands, power shortages are likely by 2030. Therefore, for the sustainable operation of smart cities, it is essential to expand power infrastructure based on the predicted data. © 2024 IEEE.
Author Keywords	Data Analysis; Electric Vehicles; Energy; Smart City; System Stability

Similar Articles

Id	Similarity	Authors	Title	Published
22524	0.892	Doda D.K.; Beemkumar N.; Awasthi A.; Gautam A.K.	Electric Vehicle Energy Management: Charging In Sustainable Urban Settings For Smart Cities	E3S Web of Conferences, 540 (2024)
23140	0.881	Almihat M.G.M.; Kahn M.T.E.; Aboalez K.; Almaktoof A.M.	Energy And Sustainable Development In Smart Cities: An Overview	Smart Cities, 5, 4 (2022)
6583	0.879	Hossain M.S.; Kumar L.; El Haj Assad M.; Alayi R.	Advancements And Future Prospects Of Electric Vehicle Technologies: A Comprehensive Review	Complexity, 2022 (2022)
40803	0.877	Fernandez V.; Pérez V.; Roig R.	Optimizing Energy Supply For Full Electric Vehicles In Smart Cities: A Comprehensive Mobility Network Model	World Electric Vehicle Journal, 16, 1 (2025)
22500	0.872	Ahmed I.; Basit A.; Ahmad M.; AlMuhaini M.; Khalid M.	Electric Mobility Challenges And Approaches For Sustainable Green Power Synergy In Smart Cities	Arabian Journal for Science and Engineering, 50, 8 (2025)
978	0.869	Rahman M.M.; Thill J.-C.	A Comprehensive Survey Of The Key Determinants Of Electric Vehicle Adoption: Challenges And Opportunities In The Smart City Context	World Electric Vehicle Journal, 15, 12 (2024)
26834	0.868	Cavus M.; Ayan H.; Dissanayake D.; Sharma A.; Deb S.; Bell M.	Forecasting Electric Vehicle Charging Demand In Smart Cities Using Hybrid Deep Learning Of Regional Spatial Behaviours	Energies, 18, 13 (2025)
32441	0.868	Mousavil S.; Ochoa-Zezzatti A.; Contreras-Masse R.; Torres V.; Gonzalez S.	Intelligent Model To Build An Smart Grid For Electrical Vehicles	Studies in Computational Intelligence, 806 (2025)
40628	0.867	Fernandez V.; Pérez V.	Optimization Of Electric Vehicle Charging Control In A Demand-Side Management Context: A Model Predictive Control Approach	Applied Sciences (Switzerland), 14, 19 (2024)
30607	0.865	Ghasemlou S.M.; Ochoa-Zezzatti A.; Torres V.; Martinez E.; Lopez V.	Implementation Of Time Series To Determine Purchase And Use Of Electric Cars In A Smart City Considering Generation Z As Target Population	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14502 LNAI (2024)