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A study on electricity demand forecasting for electric vehicles in KOREA
Journal of the Korean Data & Information Science Society 2018;29:1137-53
Published online September 30, 2018
© 2018 Korean Data and Information Science Society.

Suim Choi1 · Heung-gu Sohn2 · Sahm Kim3

13Department of Applied Statistics, Chung-Ang University
2Department of Aviation, The Korea Transport Institute
Correspondence to: Professor, Department of Applied Statistics, Seoul, Korea. E-mail: sahm@cau.ac.kr
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B01014954).
Received July 11, 2018; Revised September 10, 2018; Accepted September 13, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Environmental pollution issues such as global warming and particular matter (PM) have recently brought a lot of attention to the government’s eco-friendly policies all. In addition to environmentally friendly energy, various policies like subsidy support policies are being implemented to promote the use of electric vehicles. As the use of electric vehicles become active, the demand of electric power for charging electric vehicles will increase. Therefore, it is important to predict accurately the electric demand for charging electric vehicles in order to establish a smooth demand supply plan. In this paper, electric vehicle charging station data from Seoul and Jeju Island were collected and the research was conducted to forecast the amount of charging power for each region. The time series prediction models (ARIMA, ARIMAX, ARIMA-GARCH, ARIMAX-GARCH, SARIMA, SARIMAX) for electric vehicles charging power amount prediction were compared on the basis of MAPE, SMAPE.
Keywords : Charging power, EV, time series model.