DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Han Seung | ko |
dc.contributor.author | Bae, Kuk Yeol | ko |
dc.contributor.author | Park, Hong-Shik | ko |
dc.contributor.author | Sung, Dan Keun | ko |
dc.date.accessioned | 2016-09-07T01:06:50Z | - |
dc.date.available | 2016-09-07T01:06:50Z | - |
dc.date.created | 2016-04-06 | - |
dc.date.created | 2016-04-06 | - |
dc.date.issued | 2016-07 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, v.7, no.3, pp.1255 - 1263 | - |
dc.identifier.issn | 1949-3029 | - |
dc.identifier.uri | http://hdl.handle.net/10203/212485 | - |
dc.description.abstract | Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years' historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.subject | RADIATION | - |
dc.subject | REGRESSION | - |
dc.subject | FORECAST | - |
dc.subject | OUTPUT | - |
dc.subject | AEROSOL | - |
dc.subject | SERIES | - |
dc.subject | MODEL | - |
dc.title | Solar Power Prediction Based on Satellite Images and Support Vector Machine | - |
dc.type | Article | - |
dc.identifier.wosid | 000379696600035 | - |
dc.identifier.scopusid | 2-s2.0-84976488550 | - |
dc.type.rims | ART | - |
dc.citation.volume | 7 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 1255 | - |
dc.citation.endingpage | 1263 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | - |
dc.identifier.doi | 10.1109/TSTE.2016.2535466 | - |
dc.contributor.localauthor | Park, Hong-Shik | - |
dc.contributor.localauthor | Sung, Dan Keun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Solar power | - |
dc.subject.keywordAuthor | Irradiance | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Forecasting | - |
dc.subject.keywordAuthor | Photovoltaic | - |
dc.subject.keywordAuthor | Satellite images | - |
dc.subject.keywordAuthor | Support vector machine | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordPlus | RADIATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | FORECAST | - |
dc.subject.keywordPlus | OUTPUT | - |
dc.subject.keywordPlus | AEROSOL | - |
dc.subject.keywordPlus | SERIES | - |
dc.subject.keywordPlus | MODEL | - |
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