Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koo, Junmo | - |
dc.contributor.author | Han, Gwon Deok | - |
dc.contributor.author | Choi, Hyung Jong | - |
dc.contributor.author | Shim, Joon Hyung | - |
dc.date.accessioned | 2021-09-04T09:17:22Z | - |
dc.date.available | 2021-09-04T09:17:22Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-12-15 | - |
dc.identifier.issn | 0360-5442 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/91575 | - |
dc.description.abstract | In this study, we investigate the accuracy of wind-speed prediction at a designated target site using wind-speed data from reference stations that employ an ANN (artificial neural network). The reference and target sites fall into three geographical categories: plains, coast, and mountains of South Korea. Accurate wind-speed predictions are calculated by means of a correlation coefficient between the actual and simulated wind-speed data obtained by ANN. We investigate the effect of the geological characteristics of each category and the distance between reference and target sites on the accuracy of wind-speed prediction using ANN. (C) 2015 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | ENERGY | - |
dc.subject | MECHANISMS | - |
dc.subject | PENINSULA | - |
dc.subject | STATIONS | - |
dc.subject | MODEL | - |
dc.subject | POWER | - |
dc.subject | AREA | - |
dc.title | Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shim, Joon Hyung | - |
dc.identifier.doi | 10.1016/j.energy.2015.10.026 | - |
dc.identifier.scopusid | 2-s2.0-84954520579 | - |
dc.identifier.wosid | 000367409500005 | - |
dc.identifier.bibliographicCitation | ENERGY, v.93, pp.1296 - 1302 | - |
dc.relation.isPartOf | ENERGY | - |
dc.citation.title | ENERGY | - |
dc.citation.volume | 93 | - |
dc.citation.startPage | 1296 | - |
dc.citation.endPage | 1302 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Thermodynamics | - |
dc.relation.journalResearchArea | Energy & Fuels | - |
dc.relation.journalWebOfScienceCategory | Thermodynamics | - |
dc.relation.journalWebOfScienceCategory | Energy & Fuels | - |
dc.subject.keywordPlus | ENERGY | - |
dc.subject.keywordPlus | MECHANISMS | - |
dc.subject.keywordPlus | PENINSULA | - |
dc.subject.keywordPlus | STATIONS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | POWER | - |
dc.subject.keywordPlus | AREA | - |
dc.subject.keywordAuthor | Wind energy | - |
dc.subject.keywordAuthor | Artificial neural networks | - |
dc.subject.keywordAuthor | Wind prediction | - |
dc.subject.keywordAuthor | Climate data | - |
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