응용통계연구 (The Korean Journal of Applied Statistics)

  • 제26권2호
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  • Pages.349-360
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  • 2013
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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시계열 모형을 이용한 일별 최대 전력 수요 예측 연구

Daily Peak Load Forecasting for Electricity Demand by Time series Models

  • 이정순 (중앙대학교 응용통계학과) ;
  • 손흥구 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Lee, Jeong-Soon (Department of Applied Statistics, Chung-Ang University) ;
  • Sohn, H.G. (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, S. (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2013.02.26
  • 심사 : 2013.04.05
  • 발행 : 2013.04.30
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초록

최근 일별 최대 전력수요 예측은 전력설비 계획 및 운용에 매우 중요한 사안으로 주목받고 있다. 본 연구는 일별 최대 전력수요 예측을 위하여 대표적 시계열 모형을 소개하고, 예측의 성능 비교를 위하여 RMSE(Root mean squared error)와 MAPE(Mean absolute percentage error)를 사용한다. 연구결과로 보완된 Holt-Winters 모형과 Reg-ARIMA 모형이 다른 모형에 비하여 우수한 예측 성능을 보였다.

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

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참고문헌

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