New artificial neural network prediction method for electrical consumption forecasting based on building end-uses

Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electr...

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Autores: Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571, Álvarez, Carlos|||0000-0002-8238-1606, Roldán-Blay, Carlos|||0000-0001-9459-0563, Alcázar-Ortega, Manuel|||0000-0001-5384-3931
Tipo de recurso: artículo
Fecha de publicación:2011
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/45980
Acceso en línea:https://riunet.upv.es/handle/10251/45980
Access Level:acceso abierto
Palabra clave:Artificial neural networks
Building end-uses
Building energy consumption
Forecast method
Active energy
Artificial Neural Network
Commercial customers
Customer flexibility
Demand response programs
Electrical consumption
Electricity market
End-uses
Fundamental features
High energy prices
In-buildings
Load curves
Short term prediction
Total power consumption
Training data sets
Customer satisfaction
Electric load forecasting
Energy resources
Energy utilization
Forecasting
Sales
Neural networks
INGENIERIA ELECTRICA
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repository_id_str
spelling New artificial neural network prediction method for electrical consumption forecasting based on building end-usesEscrivá-Escrivá, Guillermo|||0000-0002-3202-4571Álvarez, Carlos|||0000-0002-8238-1606Roldán-Blay, Carlos|||0000-0001-9459-0563Alcázar-Ortega, Manuel|||0000-0001-5384-3931Artificial neural networksBuilding end-usesBuilding energy consumptionForecast methodActive energyArtificial Neural NetworkCommercial customersCustomer flexibilityDemand response programsElectrical consumptionElectricity marketEnd-usesFundamental featuresHigh energy pricesIn-buildingsLoad curvesShort term predictionTotal power consumptionTraining data setsCustomer satisfactionElectric load forecastingEnergy resourcesEnergy utilizationForecastingSalesNeural networksINGENIERIA ELECTRICADue to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarter-hourly values of the Universitat Politcnica de Valncia, a commercial customer consuming 11,500 kW. © 2011 Elsevier B.V. All rights reserved.This research work has been possible with the support of the Universitat Politecnica de Valencia (Spain) with grant #CE 19990032.ElsevierDepartamento de Ingeniería EléctricaInstituto Universitario de Investigación de Ingeniería EnergéticaEscuela Técnica Superior de Ingeniería IndustrialUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20112011-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/45980reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat Politècnica de València https://doi.org/10.13039/501100004233 CE-19990032open accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/459802026-06-13T07:49:27Z
dc.title.none.fl_str_mv New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
title New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
spellingShingle New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
Artificial neural networks
Building end-uses
Building energy consumption
Forecast method
Active energy
Artificial Neural Network
Commercial customers
Customer flexibility
Demand response programs
Electrical consumption
Electricity market
End-uses
Fundamental features
High energy prices
In-buildings
Load curves
Short term prediction
Total power consumption
Training data sets
Customer satisfaction
Electric load forecasting
Energy resources
Energy utilization
Forecasting
Sales
Neural networks
INGENIERIA ELECTRICA
title_short New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
title_full New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
title_fullStr New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
title_full_unstemmed New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
title_sort New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
dc.creator.none.fl_str_mv Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
Álvarez, Carlos|||0000-0002-8238-1606
Roldán-Blay, Carlos|||0000-0001-9459-0563
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
author_facet Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
Álvarez, Carlos|||0000-0002-8238-1606
Roldán-Blay, Carlos|||0000-0001-9459-0563
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author_role author
author2 Álvarez, Carlos|||0000-0002-8238-1606
Roldán-Blay, Carlos|||0000-0001-9459-0563
Alcázar-Ortega, Manuel|||0000-0001-5384-3931
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Eléctrica
Instituto Universitario de Investigación de Ingeniería Energética
Escuela Técnica Superior de Ingeniería Industrial
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Artificial neural networks
Building end-uses
Building energy consumption
Forecast method
Active energy
Artificial Neural Network
Commercial customers
Customer flexibility
Demand response programs
Electrical consumption
Electricity market
End-uses
Fundamental features
High energy prices
In-buildings
Load curves
Short term prediction
Total power consumption
Training data sets
Customer satisfaction
Electric load forecasting
Energy resources
Energy utilization
Forecasting
Sales
Neural networks
INGENIERIA ELECTRICA
topic Artificial neural networks
Building end-uses
Building energy consumption
Forecast method
Active energy
Artificial Neural Network
Commercial customers
Customer flexibility
Demand response programs
Electrical consumption
Electricity market
End-uses
Fundamental features
High energy prices
In-buildings
Load curves
Short term prediction
Total power consumption
Training data sets
Customer satisfaction
Electric load forecasting
Energy resources
Energy utilization
Forecasting
Sales
Neural networks
INGENIERIA ELECTRICA
description Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarter-hourly values of the Universitat Politcnica de Valncia, a commercial customer consuming 11,500 kW. © 2011 Elsevier B.V. All rights reserved.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-11-01
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://riunet.upv.es/handle/10251/45980
url https://riunet.upv.es/handle/10251/45980
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat Politècnica de València https://doi.org/10.13039/501100004233 CE-19990032
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403565978550272
score 15.301603

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