Short-term multinodal load forecasting in distribution systems using general regression neural networks
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PTC.2011.6019432 http://hdl.handle.net/11449/72742 |
Resumo: | Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE. |
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Repositório Institucional da UNESP |
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Short-term multinodal load forecasting in distribution systems using general regression neural networksBus Load ForecastingGeneral Regression Neural NetworkShort-Term Load ForecastingDistribution systemsElectrical networksGeneral regression neural networkGlobal loadsLoad forecastingLoad participationLocal loadsNew zealandForecastingIntelligent systemsNeural networksRegression analysisSustainable developmentElectric load forecastingMultinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 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.2011.60194322011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.http://hdl.handle.net/11449/7274210.1109/PTC.2011.60194322-s2.0-800533704977166279400544764Scopusreponame: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/openAccess2024-07-04T19:11:50Zoai:repositorio.unesp.br:11449/72742Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:32:58.862097Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
title |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
spellingShingle |
Short-term multinodal load forecasting in distribution systems using general regression neural networks Nose-Filho, K. [UNESP] Bus Load Forecasting General Regression Neural Network Short-Term Load Forecasting Distribution systems Electrical networks General regression neural network Global loads Load forecasting Load participation Local loads New zealand Forecasting Intelligent systems Neural networks Regression analysis Sustainable development Electric load forecasting |
title_short |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
title_full |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
title_fullStr |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
title_full_unstemmed |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
title_sort |
Short-term multinodal load forecasting in distribution systems using general regression neural networks |
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 |
Bus Load Forecasting General Regression Neural Network Short-Term Load Forecasting Distribution systems Electrical networks General regression neural network Global loads Load forecasting Load participation Local loads New zealand Forecasting Intelligent systems Neural networks Regression analysis Sustainable development Electric load forecasting |
topic |
Bus Load Forecasting General Regression Neural Network Short-Term Load Forecasting Distribution systems Electrical networks General regression neural network Global loads Load forecasting Load participation Local loads New zealand Forecasting Intelligent systems Neural networks Regression analysis Sustainable development Electric load forecasting |
description |
Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 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.2011.6019432 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. http://hdl.handle.net/11449/72742 10.1109/PTC.2011.6019432 2-s2.0-80053370497 7166279400544764 |
url |
http://dx.doi.org/10.1109/PTC.2011.6019432 http://hdl.handle.net/11449/72742 |
identifier_str_mv |
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. 10.1109/PTC.2011.6019432 2-s2.0-80053370497 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_ |
1808129333459419136 |