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Kronghinlad, Chartrin
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Computer Science & IT Electrical & Electronics Engineering

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Impact of electric vehicle demand forecasting on charging station infrastructure development Kronghinlad, Chartrin; Nilsiam, Yuenyong; Bhumpenpein, Nalinpat; Nuchitprasitchai, Siranee; Tangprasert, Sakchai
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1010-1019

Abstract

This research addresses the challenge of forecasting electric vehicle (EV) demand in Thailand and its influence on the development of charging infrastructure. To improve predictive capability in environments with restricted historical data, we employed the grey model (GM) and genetic algorithms (GA) both independently and in combination. Using EV registration records from 2019 to 2023 obtained from the Automotive Information Center of Thailand, the optimized GM-GA hybrid model achieved markedly superior accuracy, with a mean absolute error (MAE) of 0.0016 and root mean squared error (RMSE) of 0.0031. These results demonstrate the model’s capacity to deliver precise forecasts despite data limitations, making it a valuable decision-making tool for charging station planning and deployment. The outcomes underscore the importance of forward-looking infrastructure strategies to support the growth of Thailand’s EV market and its transition toward sustainable mobility.
Co-Authors Bhumpenpein, Nalinpat Nilsiam, Yuenyong Nuchitprasitchai, Siranee Tangprasert, Sakchai