The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Forecasting daily peak load by time series model with temperature and special days effect

기온과 특수일 효과를 고려하여 시계열 모형을 활용한 일별 최대 전력 수요 예측 연구

  • Lee, Jin Young (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
  • 이진영 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Received : 2019.01.10
  • Accepted : 2019.01.22
  • Published : 2019.02.28
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Abstract

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

일별 최대전력 수요 예측은 국가의 전력 수급운영에 중요한 과제로서 과거부터 다양한 방법들이 끊임없이 연구되어 왔다. 일별 최대전력 수요를 정확히 예측함으로써 발전설비에 대한 일일 운용계획을 작성하고 효율적인 설비 운용을 통해 불필요한 에너지 자원의 소비를 감소하는데 기여할 수 있으며 여름 겨울철 냉난방수요로 인해 발생하는 전력소비 과다로 인한 전력예비율 감소 문제 등에 선제적으로 대비할 수 있는 장점을 가진다. 이러한 일별 최대전력수요 예측을 위하여 본 논문에서는 Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, NNETAR 모형에 평일, 주말, 특수일에 대한 효과와 온도에 대한 영향을 함께 고려하여 다음날의 일별 최대전력을 예측하는 모형을 연구하였다. 본 논문을 통한 모형들의 예측 성능 평가 결과 요일, 온도를 고려할 수 있는 Seasonal Reg-ARIMA 모형과 NNETAR 모형이 이를 고려할 수 없는 다른 시계열 모형보다 우수한 예측 성능을 나타내었고 그 중 인공신경망을 활용한 NNETAR 모형의 예측 성능이 가장 우수하였다.

Keywords

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Figure 2.1. Multi-layer neural network.

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Figure 3.1. Time series plot of daily peak load.

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Figure 3.2. Time series plot of daily peak load on March 2017.

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Figure 3.3. Daily peak load forecast of ARIMA.

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Figure 3.4. Daily peak load forecast of ARIMAX.

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Figure 3.5. Daily peak load forecast of TBATS.

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Figure 3.6. Daily peak load forecast of NNETAR.

Table 3.1. Parameter estimate of ARIMA

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Table 3.2. Parameter estimate of ARIMAX

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Table 3.3. Parameter estimate of TBATS

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Table 3.4. Forecast accuracy of models

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