Enhancing neural network based load forecasting via preprocessing.

Detalhes bibliográficos
Autor(a) principal: Silva, Alexandre Pinto Alves da
Data de Publicação: 2001
Outros Autores: Reis, Agnaldo José da Rocha, El-Sharkawi, Mohamed A., Marks II, Robert J.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UFOP
Texto Completo: http://www.repositorio.ufop.br/handle/123456789/1198
Resumo: The importance of Short-Term Load Forecasting (STLF) has increased, lately. With deregulation and competition, energy price forecasting has become a big business. Load bus forecasting is essential for feeding the analytical methods used for determining energy prices. The variability and nonstationarity of loads are getting worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting specialists. More autonomous load predictors are needed in the new competitive scenario. Despite the success of neural network based STLF, techniques for preprocessing the load data have been overlooked. In this paper, different techniques for preprocessing a load series have been investigated. The main goal is to induce stationarity and to emphasize the relevant features of the series in order to produce more robust load forecasters. One year of load data from a Brazilian electric utility has been used to validate the proposed
id UFOP_4375ddd56681e5780a611e9936580b4c
oai_identifier_str oai:localhost:123456789/1198
network_acronym_str UFOP
network_name_str Repositório Institucional da UFOP
repository_id_str 3233
spelling Silva, Alexandre Pinto Alves daReis, Agnaldo José da RochaEl-Sharkawi, Mohamed A.Marks II, Robert J.2012-07-24T17:26:31Z2012-07-24T17:26:31Z2001SILVA, A. P. A. da. et al. Enhancing neural network based load forecasting via preprocessing. In: IEEE ISAP. 2001. Budapest, Hungary, Anais... Budapest: IEEE ISAP, 2001. p. 118-123. Disponível em: <http://www.marksmannet.com/RobertMarks/REPRINTS/2001_EnhancingNetworkBasedLoadForecasting.pdf>. Acesso em: 24 jul. 2012http://www.repositorio.ufop.br/handle/123456789/1198The importance of Short-Term Load Forecasting (STLF) has increased, lately. With deregulation and competition, energy price forecasting has become a big business. Load bus forecasting is essential for feeding the analytical methods used for determining energy prices. The variability and nonstationarity of loads are getting worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting specialists. More autonomous load predictors are needed in the new competitive scenario. Despite the success of neural network based STLF, techniques for preprocessing the load data have been overlooked. In this paper, different techniques for preprocessing a load series have been investigated. The main goal is to induce stationarity and to emphasize the relevant features of the series in order to produce more robust load forecasters. One year of load data from a Brazilian electric utility has been used to validate the proposedDigital filtersNeural networksLoad ForescastingEnhancing neural network based load forecasting via preprocessing.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://www.repositorio.ufop.br/bitstream/123456789/1198/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD55ORIGINALEVENTO_EnhancingNeuralNetwork.pdfEVENTO_EnhancingNeuralNetwork.pdfapplication/pdf640331http://www.repositorio.ufop.br/bitstream/123456789/1198/1/EVENTO_EnhancingNeuralNetwork.pdfd651fb949303ae58227220a2276aae5cMD51123456789/11982019-02-28 09:40:17.861oai:localhost: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Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332019-02-28T14:40:17Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false
dc.title.pt_BR.fl_str_mv Enhancing neural network based load forecasting via preprocessing.
title Enhancing neural network based load forecasting via preprocessing.
spellingShingle Enhancing neural network based load forecasting via preprocessing.
Silva, Alexandre Pinto Alves da
Digital filters
Neural networks
Load Forescasting
title_short Enhancing neural network based load forecasting via preprocessing.
title_full Enhancing neural network based load forecasting via preprocessing.
title_fullStr Enhancing neural network based load forecasting via preprocessing.
title_full_unstemmed Enhancing neural network based load forecasting via preprocessing.
title_sort Enhancing neural network based load forecasting via preprocessing.
author Silva, Alexandre Pinto Alves da
author_facet Silva, Alexandre Pinto Alves da
Reis, Agnaldo José da Rocha
El-Sharkawi, Mohamed A.
Marks II, Robert J.
author_role author
author2 Reis, Agnaldo José da Rocha
El-Sharkawi, Mohamed A.
Marks II, Robert J.
author2_role author
author
author
dc.contributor.author.fl_str_mv Silva, Alexandre Pinto Alves da
Reis, Agnaldo José da Rocha
El-Sharkawi, Mohamed A.
Marks II, Robert J.
dc.subject.por.fl_str_mv Digital filters
Neural networks
Load Forescasting
topic Digital filters
Neural networks
Load Forescasting
description The importance of Short-Term Load Forecasting (STLF) has increased, lately. With deregulation and competition, energy price forecasting has become a big business. Load bus forecasting is essential for feeding the analytical methods used for determining energy prices. The variability and nonstationarity of loads are getting worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting specialists. More autonomous load predictors are needed in the new competitive scenario. Despite the success of neural network based STLF, techniques for preprocessing the load data have been overlooked. In this paper, different techniques for preprocessing a load series have been investigated. The main goal is to induce stationarity and to emphasize the relevant features of the series in order to produce more robust load forecasters. One year of load data from a Brazilian electric utility has been used to validate the proposed
publishDate 2001
dc.date.issued.fl_str_mv 2001
dc.date.accessioned.fl_str_mv 2012-07-24T17:26:31Z
dc.date.available.fl_str_mv 2012-07-24T17:26:31Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.citation.fl_str_mv SILVA, A. P. A. da. et al. Enhancing neural network based load forecasting via preprocessing. In: IEEE ISAP. 2001. Budapest, Hungary, Anais... Budapest: IEEE ISAP, 2001. p. 118-123. Disponível em: <http://www.marksmannet.com/RobertMarks/REPRINTS/2001_EnhancingNetworkBasedLoadForecasting.pdf>. Acesso em: 24 jul. 2012
dc.identifier.uri.fl_str_mv http://www.repositorio.ufop.br/handle/123456789/1198
identifier_str_mv SILVA, A. P. A. da. et al. Enhancing neural network based load forecasting via preprocessing. In: IEEE ISAP. 2001. Budapest, Hungary, Anais... Budapest: IEEE ISAP, 2001. p. 118-123. Disponível em: <http://www.marksmannet.com/RobertMarks/REPRINTS/2001_EnhancingNetworkBasedLoadForecasting.pdf>. Acesso em: 24 jul. 2012
url http://www.repositorio.ufop.br/handle/123456789/1198
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFOP
instname:Universidade Federal de Ouro Preto (UFOP)
instacron:UFOP
instname_str Universidade Federal de Ouro Preto (UFOP)
instacron_str UFOP
institution UFOP
reponame_str Repositório Institucional da UFOP
collection Repositório Institucional da UFOP
bitstream.url.fl_str_mv http://www.repositorio.ufop.br/bitstream/123456789/1198/5/license.txt
http://www.repositorio.ufop.br/bitstream/123456789/1198/1/EVENTO_EnhancingNeuralNetwork.pdf
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
d651fb949303ae58227220a2276aae5c
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)
repository.mail.fl_str_mv repositorio@ufop.edu.br
_version_ 1801685734096109568

Registros relacionados