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Analysis of time series to support decision making on V2G using energy consumption data
Journal of the Korean Data & Information Science Society 2019;30:401-14
Published online March 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.2.401
© 2019 Korean Data and Information Science Society.

Jeong Hyun Lee1 · Sang Jin Oh2 · Yeochang Yoon3 · Young Ho Ahn4 · Jae Sung Kim5 · Wan Sup Cho6 · Sung Duck Lee7

156Department of Bigdata, Chungbuk National University, 27Department of Information Statistics, Chungbuk National University, 3Woosuk University, 4RETIGRID
Correspondence to: Professor, Department of Information Statistics, Chungbuk National University, 1 ChungDae-ro, Seowon-gu, Chungbuk, Korea. E-mail: sdlee@chungbuk.ac.kr
This research is supported by a reseach fund of university ICT center on Ministry of Science and ICT/IITP(IITP-2018-0-01396).
Received December 31, 2018; Revised January 21, 2019; Accepted January 22, 2019.
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
The domestic energy demand is currently in crisis, and this is related with the phase-out of nuclear power plant and expansions of EV charging stations. Moreover, the additional demand will be increased because of the political issues of the Korean peninsula, too. Many studies have focus on and dealt with alternative energy development, energy efficiency and energy storage system. In this study, we analyzed time series data of the energy consumption. As the result, we recognized the importance of predicting the amount of energy peak load and its time zone when it is conversed from main power to ESS, and energy trading. Therefore our goal is to get more reliable results through a short time period analysis based on 24 hours. In order to conduct this research study, Seasonal ARIMA (SARIMA) model and seasonal exponential smoothing method were used. We hope this analyzed result will be utilized positively on real environment of V2G or ESS in the future.
Keywords : ESS, Energy peak load, Seasonal ARIMA, Seasonal exponential smoothing, V2G.