Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant
DC Field | Value | Language |
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dc.contributor.author | Ahn, Gilseung | - |
dc.contributor.author | Hur, Sun | - |
dc.date.accessioned | 2021-06-22T16:43:26Z | - |
dc.date.available | 2021-06-22T16:43:26Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2016-06 | - |
dc.identifier.issn | 1598-7248 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13633 | - |
dc.description.abstract | Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | KOREAN INST INDUSTRIAL ENGINEERS | - |
dc.title | Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hur, Sun | - |
dc.identifier.doi | 10.7232/iems.2016.15.2.148 | - |
dc.identifier.scopusid | 2-s2.0-84979306629 | - |
dc.identifier.wosid | 000391000400004 | - |
dc.identifier.bibliographicCitation | INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, v.15, no.2, pp.148 - 155 | - |
dc.relation.isPartOf | INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS | - |
dc.citation.title | INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS | - |
dc.citation.volume | 15 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 148 | - |
dc.citation.endPage | 155 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002123032 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.subject.keywordPlus | OUTPUT | - |
dc.subject.keywordAuthor | Continuous Conditional Random Field | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | Combined Cycle Power Plant | - |
dc.subject.keywordAuthor | Energy Saving | - |
dc.subject.keywordAuthor | Prediction | - |
dc.identifier.url | http://koreascience.or.kr/article/JAKO201620853200476.page | - |
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