Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment
DC Field | Value | Language |
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dc.contributor.author | Nam, Woochul | - |
dc.contributor.author | Oh, Ki-Yong | - |
dc.date.accessioned | 2022-01-19T02:40:54Z | - |
dc.date.available | 2022-01-19T02:40:54Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53825 | - |
dc.description.abstract | Evaluating the economic feasibility of wind farms via long-term wind-resource assessments is indispensable because short-term data measured at a candidate wind-farm site cannot represent the long-term wind potential. Prediction errors are significant when seasonal and year-on-year variations occur. Moreover, reliable long-term reference data with a high correlation to short-term measured data are often unavailable. This paper presents an alternative solution to predict long-term wind resources for a site exhibiting seasonal and year-on-year variations, where long-term reference data are unavailable. An analysis shows that a mutually complementary measure-correlate-predict method can be employed, because several datasets obtained over short periods are used to correct long-term wind resource data in a mutually complementary manner. Moreover, this method is useful in evaluating extreme wind speeds, which is one of the main factors affecting site compliance evaluation and the selection of a suitable wind turbine class based on the International Electrotechnical Commission standards. The analysis also shows that energy density is a more sensitive metric than wind speed for sites with seasonal and year-on-year variations because of the wide distribution of wind speeds. A case study with short-term data measured at Fujeij, Jordan, clearly identifies the factors necessary to perform the reliable and accurate assessment of long-term wind potentials. | - |
dc.format.extent | 20 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/math8101795 | - |
dc.identifier.bibliographicCitation | MATHEMATICS, v.8, no.10, pp 1 - 20 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000582856800001 | - |
dc.identifier.scopusid | 2-s2.0-85093068491 | - |
dc.citation.endPage | 20 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | MATHEMATICS | - |
dc.citation.volume | 8 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | measure-correlate-predict | - |
dc.subject.keywordAuthor | site compliance | - |
dc.subject.keywordAuthor | wind-resource assessment | - |
dc.subject.keywordAuthor | wind potential prediction | - |
dc.subject.keywordPlus | KOREAN PENINSULA | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | FARM | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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