Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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
이동현정아현김진영김창기김현구이영섭
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Natural Science > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Yung Seop photo

Lee, Yung Seop
College of Natural Science (Department of Statistics)
Read more

Altmetrics

Total Views & Downloads

BROWSE