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최대 전력수요 예측을 위한 시계열모형 비교

Comparison of time series predictions for maximum electric power demand

  • 권숙희 (충북대학교 정보통계학과) ;
  • 김재훈 (충북대학교 정보통계학과) ;
  • 손석만 (충북대학교 정보통계학과) ;
  • 이성덕 (충북대학교 정보통계학과)
  • Kwon, Sukhui (Department of Information & Statistics, Chungbuk National University) ;
  • Kim, Jaehoon (Department of Information & Statistics, Chungbuk National University) ;
  • Sohn, SeokMan (Department of Information & Statistics, Chungbuk National University) ;
  • Lee, SungDuck (Department of Information & Statistics, Chungbuk National University)
  • 투고 : 2021.06.15
  • 심사 : 2021.07.07
  • 발행 : 2021.08.31

초록

본 연구에서는 여러가지 시계열 모형 중 평활법(가법계절지수, 승법계절지수), 계절 ARIMA 모형, ARARCH 그리고 AR-GARCH 회귀모형을 이용하여 최대 전력수요를 예측하는 방법을 연구하였다. 이 때 가중 평균모형으로 추세를 갖는 시계열 모형과 온도에 대한 회귀 모형을 적절한 가중치로 예측 정확도를 높이는 방법도 연구하였다. 결과적으로 AR-GARCH 회귀모형으로 예측하는 것이 가중 우수함을 보였다.

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

키워드

과제정보

분석을 도와준 숙명여자대학교 정선아 대학원생에게 진심으로 감사드립니다.

참고문헌

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