Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
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Solar Power Generation Forecast Model Using Seasonal ARIMA

SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축

  • Lee, Dong-Hyun (Department of Statistics, Dongguk University) ;
  • Jung, Ahyun (Department of Statistics, Dongguk University) ;
  • Kim, Jin-Young (Department of New and Renewable Energy Resource&Policy Center, Korea Institute of Energy Research) ;
  • Kim, Chang Ki (Department of New and Renewable Energy Resource&Policy Center, Korea Institute of Energy Research) ;
  • Kim, Hyun-Goo (Department of New and Renewable Energy Resource&Policy Center, Korea Institute of Energy Research) ;
  • Lee, Yung-Seop (Department of Statistics, Dongguk University)
  • 이동현 (동국대학교 일반대학원 통계학과) ;
  • 정아현 (동국대학교 일반대학원 통계학과) ;
  • 김진영 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김창기 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김현구 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 이영섭 (동국대학교 통계학과)
  • Received : 2019.05.21
  • Accepted : 2019.06.14
  • Published : 2019.06.30
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Abstract

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

Keywords

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Fig. 1 Overview of training data set and test data set for solar power data

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Fig. 2 The actual value and the predicted value(from 1 hour to 3 hour) of solar power generation at station 4 using ARIMA model

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Fig. 3 The actual value and the predicted value (from 1 hour to 3 hour) of solar power generation at station 4 using SARIMA model

Table 1 ARIMA model for 4 stations

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Table 2 SARIMA model for 4 stations

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Table 3 Comparison of MAE between ARIMA model and SARIMA model

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Table 4 Comparison of RMSE between ARIMA model and SARIMA model

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