Enhancing neural network based load forecasting via preprocessing.
Autor(a) principal: | |
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Data de Publicação: | 2001 |
Outros Autores: | , , |
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 |
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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 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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