Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340 |
Resumo: | The objective of the research reported in this paper is the development of a model for short term load forecasting for use in an environment characterized by uncertainty. The fundamental requirement for the proposed model is the production of robust and accurate performance with minimal computational and data resources. Our solution strategy was developed around a computational intelligence method which exploits knowledge using fuzzy logic and decision tree based techniques. The model was developed and evaluated using three years data (i.e. 2004, 2005 and 2006) on electric loads obtained from the National Control Centre (NCC) Òs.ogbo, Nigeria and was implemented using the Fuzzy Decision Tree software (FID 4.2). The data was supported by knowledge elicited from experienced power monitoring staff at NCC. The results showed that the average fractional forecast errors for the proposed model on selected data from the three years was 0.17 while that of the conventional multiple regression model was 0.80. |
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INFOCOMP: Jornal de Ciência da Computação |
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Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approachShort term load forecastingFuzzy decision treeuncertain domain.The objective of the research reported in this paper is the development of a model for short term load forecasting for use in an environment characterized by uncertainty. The fundamental requirement for the proposed model is the production of robust and accurate performance with minimal computational and data resources. Our solution strategy was developed around a computational intelligence method which exploits knowledge using fuzzy logic and decision tree based techniques. The model was developed and evaluated using three years data (i.e. 2004, 2005 and 2006) on electric loads obtained from the National Control Centre (NCC) Òs.ogbo, Nigeria and was implemented using the Fuzzy Decision Tree software (FID 4.2). The data was supported by knowledge elicited from experienced power monitoring staff at NCC. The results showed that the average fractional forecast errors for the proposed model on selected data from the three years was 0.17 while that of the conventional multiple regression model was 0.80.Editora da UFLA2011-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340INFOCOMP Journal of Computer Science; Vol. 10 No. 4 (2011): December, 2011; 29-391982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340/324Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessÍyàndá, Abímbólá R.Odéjobí, Odétúnjí A.Kómoláfé, A. O.2015-07-29T12:25:08Zoai:infocomp.dcc.ufla.br:article/340Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:32.908286INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
title |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
spellingShingle |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach Íyàndá, Abímbólá R. Short term load forecasting Fuzzy decision tree uncertain domain. |
title_short |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
title_full |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
title_fullStr |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
title_full_unstemmed |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
title_sort |
Short-term electric load forecasting in uncertain domain: A fuzzy decision tree approach |
author |
Íyàndá, Abímbólá R. |
author_facet |
Íyàndá, Abímbólá R. Odéjobí, Odétúnjí A. Kómoláfé, A. O. |
author_role |
author |
author2 |
Odéjobí, Odétúnjí A. Kómoláfé, A. O. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Íyàndá, Abímbólá R. Odéjobí, Odétúnjí A. Kómoláfé, A. O. |
dc.subject.por.fl_str_mv |
Short term load forecasting Fuzzy decision tree uncertain domain. |
topic |
Short term load forecasting Fuzzy decision tree uncertain domain. |
description |
The objective of the research reported in this paper is the development of a model for short term load forecasting for use in an environment characterized by uncertainty. The fundamental requirement for the proposed model is the production of robust and accurate performance with minimal computational and data resources. Our solution strategy was developed around a computational intelligence method which exploits knowledge using fuzzy logic and decision tree based techniques. The model was developed and evaluated using three years data (i.e. 2004, 2005 and 2006) on electric loads obtained from the National Control Centre (NCC) Òs.ogbo, Nigeria and was implemented using the Fuzzy Decision Tree software (FID 4.2). The data was supported by knowledge elicited from experienced power monitoring staff at NCC. The results showed that the average fractional forecast errors for the proposed model on selected data from the three years was 0.17 while that of the conventional multiple regression model was 0.80. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/340/324 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 10 No. 4 (2011): December, 2011; 29-39 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874741384773632 |