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dc.contributor.authorLee, Yi-Shianen_US
dc.contributor.authorTong, Lee-Ingen_US
dc.date.accessioned2014-12-08T15:22:34Z-
dc.date.available2014-12-08T15:22:34Z-
dc.date.issued2012-06-01en_US
dc.identifier.issn0306-2619en_US
dc.identifier.urihttp://hdl.handle.net/11536/15964-
dc.description.abstractEnergy consumption is an important index of the economic development of a country. Rapid changes in industry and the economy strongly affect energy consumption. Although traditional statistical approaches yield accurate forecasts of energy consumption, they may suffer from several limitations such as the need for large data sets and the assumption of a linear formula. This work describes a novel hybrid dynamic approach that combines a dynamic grey model with genetic programming to forecast energy consumption. This proposed approach is utilized to forecast energy consumption because of its excellent accuracy, applicability to cases with limited data sets and ease of computability using mathematical software. Two case studies of energy consumption demonstrate the reliability of the proposed model. Computational results indicate that the proposed approach outperforms other models in forecasting energy consumption. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectEnergy consumptionen_US
dc.subjectGrey forecasting modelen_US
dc.subjectGenetic programmingen_US
dc.subjectHybrid dynamic approachen_US
dc.titleForecasting nonlinear time series of energy consumption using a hybrid dynamic modelen_US
dc.typeArticleen_US
dc.identifier.journalAPPLIED ENERGYen_US
dc.citation.volume94en_US
dc.citation.issueen_US
dc.citation.epage251en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000302842800028-
dc.citation.woscount16-
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