건물의 일사에너지 예측을 위한 경사면 일사량 측정과 예측모델과의 비교분석 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤갑천 | - |
dc.contributor.author | 전종욱 | - |
dc.contributor.author | 김강수 | - |
dc.date.accessioned | 2021-09-07T01:46:52Z | - |
dc.date.available | 2021-09-07T01:46:52Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 1598-6411 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/109900 | - |
dc.description.abstract | In all building‘s energy simulation applications,solar radiation must be calculated on inclined surfaces. Various solar radiation models based on measured data of specific region were use dinbuilding simulation programs.Therefore,we should choose the appropriate solar radiation model for Seoul. The purpose of this study is to compare four solar radiation models on inclined surfaces that are widely used in building energy simulations.In this case,it can be said the appropriate model in Seoul is the Isotropic model. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국태양에너지학회 | - |
dc.title | 건물의 일사에너지 예측을 위한 경사면 일사량 측정과 예측모델과의 비교분석 연구 | - |
dc.title.alternative | Comparative Analysis of Measurements and Total Solar Irradiance Models on Inclined Surface for Building Solar Energy Prediction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 김강수 | - |
dc.identifier.bibliographicCitation | 한국태양에너지학회 논문집, v.32, no.6, pp.44 - 52 | - |
dc.relation.isPartOf | 한국태양에너지학회 논문집 | - |
dc.citation.title | 한국태양에너지학회 논문집 | - |
dc.citation.volume | 32 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 44 | - |
dc.citation.endPage | 52 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001730873 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | 일사량 | - |
dc.subject.keywordAuthor | 경사면 | - |
dc.subject.keywordAuthor | 예측모델 | - |
dc.subject.keywordAuthor | solarirradiance | - |
dc.subject.keywordAuthor | inclinedsurface | - |
dc.subject.keywordAuthor | predictionmodel | - |
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