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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/59405
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:07:01Zoai: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:13292025-09-29 11:07:01.942SEDICI (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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/59405 |
url |
http://sedici.unlp.edu.ar/handle/10915/59405 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
eu_rights_str_mv |
openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 57-66 |
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