Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model
DC Field | Value | Language |
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dc.contributor.author | Son, Hyojoo | - |
dc.contributor.author | Kim, Changmin | - |
dc.contributor.author | Kim, Changwan | - |
dc.contributor.author | Kang, Youngcheol | - |
dc.date.available | 2019-03-08T16:56:51Z | - |
dc.date.issued | 2015-08 | - |
dc.identifier.issn | 1392-3730 | - |
dc.identifier.issn | 1822-3605 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9223 | - |
dc.description.abstract | Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | VILNIUS GEDIMINAS TECH UNIV | - |
dc.title | Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model | - |
dc.type | Article | - |
dc.identifier.doi | 10.3846/13923730.2014.893908 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, v.21, no.6, pp 748 - 760 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000355816400007 | - |
dc.identifier.scopusid | 2-s2.0-84930813634 | - |
dc.citation.endPage | 760 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 748 | - |
dc.citation.title | JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT | - |
dc.citation.volume | 21 | - |
dc.type.docType | Article | - |
dc.publisher.location | 리투아니아 | - |
dc.subject.keywordAuthor | sustainable development | - |
dc.subject.keywordAuthor | support vector machine model | - |
dc.subject.keywordAuthor | energy consumption prediction | - |
dc.subject.keywordAuthor | government-owned building | - |
dc.subject.keywordAuthor | RReliefF variable selection | - |
dc.subject.keywordPlus | NEURAL-NETWORK MODELS | - |
dc.subject.keywordPlus | COMMERCIAL BUILDINGS | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | BENCHMARKING | - |
dc.subject.keywordPlus | APPROXIMATION | - |
dc.subject.keywordPlus | CLASSIFIER | - |
dc.subject.keywordPlus | EFFICIENCY | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | STRENGTH | - |
dc.subject.keywordPlus | CONCRETE | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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