Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset

Autores
Defilippo, Samuel B.; Neto, Guilherme G.; Hippert, Henrique S.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Brasil
Redes Neurales (Computación)
Algoritmos
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/59405
Acceder
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network_name_str SEDICI (UNLP)
spelling Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian datasetDefilippo, Samuel B.Neto, Guilherme G.Hippert, Henrique S.Ciencias InformáticasBrasilRedes Neurales (Computación)AlgoritmosThis paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2015-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf57-66http://sedici.unlp.edu.ar/handle/10915/59405enginfo:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/sio57-66.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7550info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:48:28Zoai:sedici.unlp.edu.ar:10915/59405Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:48:28.472SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
title Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
spellingShingle Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
Defilippo, Samuel B.
Ciencias Informáticas
Brasil
Redes Neurales (Computación)
Algoritmos
title_short Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
title_full Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
title_fullStr Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
title_full_unstemmed Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
title_sort Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
dc.creator.none.fl_str_mv Defilippo, Samuel B.
Neto, Guilherme G.
Hippert, Henrique S.
author Defilippo, Samuel B.
author_facet Defilippo, Samuel B.
Neto, Guilherme G.
Hippert, Henrique S.
author_role author
author2 Neto, Guilherme G.
Hippert, Henrique S.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Brasil
Redes Neurales (Computación)
Algoritmos
topic Ciencias Informáticas
Brasil
Redes Neurales (Computación)
Algoritmos
dc.description.none.fl_txt_mv This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution 3.0 Unported (CC BY 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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