Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

Detalhes bibliográficos
Autor(a) principal: Nose-Filho, K. [UNESP]
Data de Publicação: 2011
Outros Autores: Lotufo, A. D P [UNESP], Minussi, C. R. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PTC.2011.6019428
http://hdl.handle.net/11449/72741
Resumo: This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.
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spelling Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filterArtificial Neural NetworksMoving Average FilterShort Term Load ForecastingSignal ProcessingTraining DatasetAbnormal dataElectrical substationsFilter-basedGeneral regression neural networkLoad dataLoad forecastingMissing dataMoving average filterNew zealandForecastingNeural networksSignal processingSustainable developmentElectric load forecastingThis paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.Department of Electrical Engineering College of Engineering of Ilha Solteira (UNESP), Ilha Solteira, SPDepartment of Electrical Engineering College of Engineering of Ilha Solteira (UNESP), Ilha Solteira, SPUniversidade Estadual Paulista (Unesp)Nose-Filho, K. [UNESP]Lotufo, A. D P [UNESP]Minussi, C. R. [UNESP]2014-05-27T11:26:03Z2014-05-27T11:26:03Z2011-10-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PTC.2011.60194282011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.http://hdl.handle.net/11449/7274110.1109/PTC.2011.60194282-s2.0-800533500917166279400544764Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011info:eu-repo/semantics/openAccess2024-07-04T19:11:33Zoai:repositorio.unesp.br:11449/72741Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:28:26.645212Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
title Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
spellingShingle Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
Nose-Filho, K. [UNESP]
Artificial Neural Networks
Moving Average Filter
Short Term Load Forecasting
Signal Processing
Training Dataset
Abnormal data
Electrical substations
Filter-based
General regression neural network
Load data
Load forecasting
Missing data
Moving average filter
New zealand
Forecasting
Neural networks
Signal processing
Sustainable development
Electric load forecasting
title_short Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
title_full Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
title_fullStr Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
title_full_unstemmed Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
title_sort Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
author Nose-Filho, K. [UNESP]
author_facet Nose-Filho, K. [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
author_role author
author2 Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Nose-Filho, K. [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
dc.subject.por.fl_str_mv Artificial Neural Networks
Moving Average Filter
Short Term Load Forecasting
Signal Processing
Training Dataset
Abnormal data
Electrical substations
Filter-based
General regression neural network
Load data
Load forecasting
Missing data
Moving average filter
New zealand
Forecasting
Neural networks
Signal processing
Sustainable development
Electric load forecasting
topic Artificial Neural Networks
Moving Average Filter
Short Term Load Forecasting
Signal Processing
Training Dataset
Abnormal data
Electrical substations
Filter-based
General regression neural network
Load data
Load forecasting
Missing data
Moving average filter
New zealand
Forecasting
Neural networks
Signal processing
Sustainable development
Electric load forecasting
description This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.
publishDate 2011
dc.date.none.fl_str_mv 2011-10-05
2014-05-27T11:26:03Z
2014-05-27T11:26:03Z
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.uri.fl_str_mv http://dx.doi.org/10.1109/PTC.2011.6019428
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.
http://hdl.handle.net/11449/72741
10.1109/PTC.2011.6019428
2-s2.0-80053350091
7166279400544764
url http://dx.doi.org/10.1109/PTC.2011.6019428
http://hdl.handle.net/11449/72741
identifier_str_mv 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.
10.1109/PTC.2011.6019428
2-s2.0-80053350091
7166279400544764
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128816319561728

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