A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting

This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed...

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Autores: Kampouropoulos, Konstantinos|||0000-0002-1466-6394, Andrade Rengifo, Fabio, García Espinosa, Antonio|||0000-0003-0348-5210, Romeral Martínez, José Luis|||0000-0001-8112-8038
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/22425
Acceso en línea:https://hdl.handle.net/2117/22425
https://dx.doi.org/10.4316/AECE.2014.01002
Access Level:acceso abierto
Palabra clave:Genetic algorithms
Adaptive neuro-fuzzy inference system
Energy forecast
Genetic algorithm
Intelligent energy management systems
Programació genètica (Informàtica)
Energia -- Gestió
Àrees temàtiques de la UPC::Enginyeria electrònica
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oai_identifier_str oai:upcommons.upc.edu:2117/22425
network_acronym_str ES
network_name_str España
repository_id_str
spelling A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecastingKampouropoulos, Konstantinos|||0000-0002-1466-6394Andrade Rengifo, FabioGarcía Espinosa, Antonio|||0000-0003-0348-5210Romeral Martínez, José Luis|||0000-0001-8112-8038Genetic algorithmsAdaptive neuro-fuzzy inference systemEnergy forecastGenetic algorithmIntelligent energy management systemsProgramació genètica (Informàtica)Energia -- GestióÀrees temàtiques de la UPC::Enginyeria electrònicaThis document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes.20142014-02-0120142014-03-28journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/22425https://dx.doi.org/10.4316/AECE.2014.01002reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/224252026-05-27T15:37:01Z
dc.title.none.fl_str_mv A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
title A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
spellingShingle A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
Kampouropoulos, Konstantinos|||0000-0002-1466-6394
Genetic algorithms
Adaptive neuro-fuzzy inference system
Energy forecast
Genetic algorithm
Intelligent energy management systems
Programació genètica (Informàtica)
Energia -- Gestió
Àrees temàtiques de la UPC::Enginyeria electrònica
title_short A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
title_full A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
title_fullStr A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
title_full_unstemmed A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
title_sort A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
dc.creator.none.fl_str_mv Kampouropoulos, Konstantinos|||0000-0002-1466-6394
Andrade Rengifo, Fabio
García Espinosa, Antonio|||0000-0003-0348-5210
Romeral Martínez, José Luis|||0000-0001-8112-8038
author Kampouropoulos, Konstantinos|||0000-0002-1466-6394
author_facet Kampouropoulos, Konstantinos|||0000-0002-1466-6394
Andrade Rengifo, Fabio
García Espinosa, Antonio|||0000-0003-0348-5210
Romeral Martínez, José Luis|||0000-0001-8112-8038
author_role author
author2 Andrade Rengifo, Fabio
García Espinosa, Antonio|||0000-0003-0348-5210
Romeral Martínez, José Luis|||0000-0001-8112-8038
author2_role author
author
author
dc.subject.none.fl_str_mv Genetic algorithms
Adaptive neuro-fuzzy inference system
Energy forecast
Genetic algorithm
Intelligent energy management systems
Programació genètica (Informàtica)
Energia -- Gestió
Àrees temàtiques de la UPC::Enginyeria electrònica
topic Genetic algorithms
Adaptive neuro-fuzzy inference system
Energy forecast
Genetic algorithm
Intelligent energy management systems
Programació genètica (Informàtica)
Energia -- Gestió
Àrees temàtiques de la UPC::Enginyeria electrònica
description This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-02-01
2014
2014-03-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/22425
https://dx.doi.org/10.4316/AECE.2014.01002
url https://hdl.handle.net/2117/22425
https://dx.doi.org/10.4316/AECE.2014.01002
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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