The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Electricity Demand Forecasting for Daily Peak Load with Seasonality and Temperature Effects

계절성과 온도를 고려한 일별 최대 전력 수요 예측 연구

  • Jung, Sang-Wook (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
  • 정상욱 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Received : 2014.10.06
  • Accepted : 2014.10.07
  • Published : 2014.10.31
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Abstract

Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.

급증하고 있는 전력수요에 대한 신뢰성 있는 예측은 합리적인 전력수급계획 수립 및 운용에 있어서 매우 중대한 사안이다. 본 논문에서는 여러 시계열 모형의 비교를 통해 전력수요량과 밀접한 연관성이 있는 온도를 어떠한 형태로 고려할 것인지, 또한 4계절이 뚜렷하여 계절별 기온 차가 많이 나는 우리나라의 특성을 어떻게 고려할 것인지에 대하여 연구하였다. 모형 간 예측력을 비교하기 위하여 Mean Absolute Percentage Error(MAPE)를 사용하였다. 모형의 성능비교 결과는 냉 난방지수와 계절요인을 동시에 고려하면서 큰 변동성을 잘 고려해줄 수 있는 Reg-AR GARCH 모형이 가장 우수한 예측력을 나타냈다.

Keywords

References

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