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최유림(You-Lim Choi),임성현(Seung-Hyeon Im),정한수(Han-Soo Jung),김승모(Seung-Mo Kim),남재원(Jae-Won Nam) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Electric vehicles (EVs) are considered a great alternative for reducing carbon emissions, and South Korea is striving to increase the penetration rate of EVs through supportive policies. However, the number of EV chargers is insufficient compared to the increasing penetration rate of EVs. Given the high cost and the requirement for high voltage, selecting an accurate installation location is crucial. Therefore, we propose a machine learning-based approach to predict the appropriate location for EV chargers using data that influences the selection of charger locations on Jeju Island. Additionally, we present a method for adjusting various machine learning conditions to enable precise selection of EV charger locations with high prediction accuracy.