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해외사례 고찰을 통한 풍력 예측모형의 과거와 현재

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dc.contributor.author김하빈-
dc.contributor.author김현구-
dc.contributor.author김진영-
dc.contributor.author이영섭-
dc.date.accessioned2024-08-08T03:31:13Z-
dc.date.available2024-08-08T03:31:13Z-
dc.date.issued2016-12-
dc.identifier.issn2093-5099-
dc.identifier.issn2733-9467-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/17155-
dc.description.abstractThe importance of wind energy has been increasing. Most of countries have studied for generating effective forecasting models about wind power prediction. This study follows up research about statistical wind forecasting models from 2000 to 2016 and compares each model in several aspects. Variety of estimators measure such as mean squared error and correlation coefficient that used to compare different models are defined. Most of methods use wind speed for dependent and independent variable. The extended application of artificial neural network and ARIMA model have mainly used to predict wind power in the past. The state-of-the-art of focuses on nonlinear regression, feature selection and Ensemble methods which are different from classical ANN and ARIMA.-
dc.format.extent10-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국풍력에너지학회-
dc.title해외사례 고찰을 통한 풍력 예측모형의 과거와 현재-
dc.title.alternativePast and Present of Wind Power Prediction Models through Overseas Case Review-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.33519/kwea.2016.7.2.005-
dc.identifier.bibliographicCitation풍력에너지저널, v.7, no.2, pp 35 - 44-
dc.citation.title풍력에너지저널-
dc.citation.volume7-
dc.citation.number2-
dc.citation.startPage35-
dc.citation.endPage44-
dc.identifier.kciidART002189709-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClasskciCandi-
dc.subject.keywordAuthor풍력 예측 모형-
dc.subject.keywordAuthor예측 평가 척도-
dc.subject.keywordAuthor인공 신경망 모형-
dc.subject.keywordAuthor회귀 모형-
dc.subject.keywordAuthor시게열 모형-
dc.subject.keywordAuthorWind prediction model-
dc.subject.keywordAuthorEstimator measure-
dc.subject.keywordAuthorArtificial neural network-
dc.subject.keywordAuthorRegression model-
dc.subject.keywordAuthorARIMA-
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