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dc.contributor.authorLee, Yi-Shianen_US
dc.contributor.authorTong, Lee-Ingen_US
dc.date.accessioned2014-12-08T15:38:06Z-
dc.date.available2014-12-08T15:38:06Z-
dc.date.issued2011-01-01en_US
dc.identifier.issn0196-8904en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.enconman.2010.06.053en_US
dc.identifier.urihttp://hdl.handle.net/11536/26143-
dc.description.abstractEnergy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimize such errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model. (C) 2010 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.titleForecasting energy consumption using a grey model improved by incorporating genetic programmingen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.enconman.2010.06.053en_US
dc.identifier.journalENERGY CONVERSION AND MANAGEMENTen_US
dc.citation.volume52en_US
dc.citation.issue1en_US
dc.citation.spage147en_US
dc.citation.epage152en_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:000284746800017-
dc.citation.woscount40-
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