Detailed Information

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

SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축

Full metadata record
DC Field Value Language
dc.contributor.author이동현-
dc.contributor.author정아현-
dc.contributor.author김진영-
dc.contributor.author김창기-
dc.contributor.author김현구-
dc.contributor.author이영섭-
dc.date.accessioned2023-04-28T03:41:05Z-
dc.date.available2023-04-28T03:41:05Z-
dc.date.issued2019-06-
dc.identifier.issn1598-6411-
dc.identifier.issn2508-3562-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/8048-
dc.description.abstractNew 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.-
dc.format.extent8-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국태양에너지학회-
dc.titleSARIMA 모형을 이용한 태양광 발전량 예보 모형 구축-
dc.title.alternativeSolar Power Generation Forecast Model Using Seasonal ARIMA-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.7836/kses.2019.39.3.059-
dc.identifier.bibliographicCitation한국태양에너지학회 논문집, v.39, no.3, pp 59 - 66-
dc.citation.title한국태양에너지학회 논문집-
dc.citation.volume39-
dc.citation.number3-
dc.citation.startPage59-
dc.citation.endPage66-
dc.identifier.kciidART002481698-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClasskciCandi-
dc.subject.keywordAuthor태양광 발전량(Solar power generation)-
dc.subject.keywordAuthor시계열 분석(Time series analysis)-
dc.subject.keywordAuthorARIMA (Autoregressive intergrated moving average)-
dc.subject.keywordAuthorSARIMA(Seasonal ARIMA)-
dc.subject.keywordAuthorMAE(Mean Absolute Error)-
dc.subject.keywordAuthorRMSE(Root Mean Square Error)-
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