SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축open accessSolar Power Generation Forecast Model Using Seasonal ARIMA
- Other Titles
- Solar Power Generation Forecast Model Using Seasonal ARIMA
- Authors
- 이동현; 정아현; 김진영; 김창기; 김현구; 이영섭
- Issue Date
- Jun-2019
- Publisher
- 한국태양에너지학회
- Keywords
- 태양광 발전량(Solar power generation); 시계열 분석(Time series analysis); ARIMA (Autoregressive intergrated moving average); SARIMA(Seasonal ARIMA); MAE(Mean Absolute Error); RMSE(Root Mean Square Error)
- Citation
- 한국태양에너지학회 논문집, v.39, no.3, pp 59 - 66
- Pages
- 8
- Indexed
- KCICANDI
- Journal Title
- 한국태양에너지학회 논문집
- Volume
- 39
- Number
- 3
- Start Page
- 59
- End Page
- 66
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/8048
- DOI
- 10.7836/kses.2019.39.3.059
- ISSN
- 1598-6411
2508-3562
- 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.
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Collections - College of Natural Science > Department of Statistics > 1. Journal Articles

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