한국방재안전학회논문집 (Journal of Korean Society of Disaster and Security)

  • 제14권2호
  • /
  • Pages.35-42
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  • 2021
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  • 2466-1147(pISSN)
  • /
  • 2508-285X(eISSN)
한국방재안전학회 (Korean Society of Disaster & Security)
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Exponential Smoothing기법을 이용한 전기자동차 전력 수요량 예측에 관한 연구

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

  • 이병현 (국립강원대학교 방재전문대학원) ;
  • 정세진 (강원종합기술연구원) ;
  • 김병식 (국립강원대학교 방재전문대학원)
  • 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)
  • 투고 : 2021.05.25
  • 심사 : 2021.06.10
  • 발행 : 2021.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 전기자동차 충전시설 확충계획에 중요한 요소인 전기자동차 전력 수요량 예측정보를 생산하기 위하여 Exponential Smoothing를 이용하여 전력 수요량 예측 모형을 제안하였다. 모형의 입력자료 구축을 위하여 종속변수로 월별 시군구 전력수요량을 독립변수로 월별 시군구 충전소 보급대수, 월별 시군구 전기자동차 충전소 충전 횟수, 월별 전기자동차 등록대수 자료를 월 단위로 수집하고 수집된 7년간 자료 중 4년간 자료를 학습기간으로 3년간 자료를 검증 기간으로 적용하였다. 전기자동차 전력 수요량 예측 모형의 정확성을 검증하기위하여 통계적 방법인 Exponential Smoothing(ETS), ARIMA모형의 결과와 비교한 결과 ETS, ARIMA 각각의 오차율은 12%, 21%로 본 논문에서 제시한 ETS가 9% 더 정확하게 분석되었으며, 전기자동차 전력 수요량 예측 모형으로써 적합함을 확인하였다. 향후 이 모형을 이용한 전기자동차 충전소 설치 계획부터 운영관리 측면에서 활용될 것으로 기대한다.

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.

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과제정보

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI [2021-00312].

참고문헌

  1. Kim, C.-H. (2013). Prediction of Short-Term Electricity Load Using Multi-Seasonal Exponential Smoothing Method. Ulsan: Korea Energy Economics Institute.
  2. Kim, D.-H. (2019). Short-Term Load Forecasting Based on LSTM and CNN. Konkuk University Graduate School. Electric Engineering Department. Master's Dissertation.
  3. Kim, K.-H., Park, R.-J., Cho, S.-W., and Song, K.-B. (2017). 24-Hour Load Forecasting Algorithm Using Artificial Neural Network in Summer Weekdays. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers. 31(12): 113-119. https://doi.org/10.5207/jieie.2017.31.12.113
  4. Kim, S.-Y., Jung, H.-W., Park, J.-D., Baek, S.-M., Kim, W.-S., Chon, K.-H., and Song, K.-B. (2014). Weekly Maximum Electric Load Forecasting for 104 Weeks by Seasonal ARIMA Model. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers. 28(1): 50-56. https://doi.org/10.5207/JIEIE.2014.28.1.050
  5. Ko, C. N. and Lee, C. M. (2016). Short-term Load Forecasting Using SVR (Support Vector Regression)-based Radial Basis Function Neural Network with Dual Extended Kalman Filter. ELSEVIER Energy. 49: 413-422.
  6. Ramanarayanan, T. S., Williams, J. R., Dugas, W. A., Hauck, L. M., and McFarland, A. M. S. (1997). Using APEX to Identify Alternative Practices for Animal Waste Management. ASAE International Meeting. Paper 97-2209. pp. 1-7.
  7. Yang, Y., Che, J., Li, Y., Zhao, Y., and Zhu, S. (2016). An Incremental Electric Load Forecasting Model based on Support Vector Regression. ELSEVIER Energy. 113: 796-808.