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http://dx.doi.org/10.21729/ksds.2021.14.2.35

A Study on the Prediction of Power Demand for Electric Vehicles Using Exponential Smoothing Techniques  

Lee, Byung-Hyun (Laboratory of Climate and Smart Disaster Management, Kangwon National University)
Jung, Se-Jin (Kangwon Institute of Inclusive Technology)
Kim, Byung-Sik (Laboratory of Climate and Smart Disaster Management, Kangwon National University)
Publication Information
Journal of Korean Society of Disaster and Security / v.14, no.2, 2021 , pp. 35-42 More about this Journal
Abstract
In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.
Keywords
Electric vehicle; Electricity load; Load forecasting; ETS; ARIMA;
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