- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show simple item record

Files in this item

dc.contributor.author Escrivá-Escrivá, Guillermo es_ES
dc.contributor.author Álvarez Bel, Carlos María es_ES
dc.contributor.author Roldán Blay, Carlos es_ES
dc.contributor.author Alcázar-Ortega, Manuel es_ES
dc.date.accessioned 2015-01-12T12:01:38Z
dc.date.available 2015-01-12T12:01:38Z
dc.date.issued 2011-11
dc.identifier.issn 0378-7788
dc.identifier.uri http://hdl.handle.net/10251/45980
dc.description.abstract 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. es_ES
dc.description.sponsorship This research work has been possible with the support of the Universitat Politecnica de Valencia (Spain) with grant #CE 19990032. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Energy and Buildings es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial neural networks es_ES
dc.subject Building end-uses es_ES
dc.subject Building energy consumption es_ES
dc.subject Forecast method es_ES
dc.subject Active energy es_ES
dc.subject Artificial Neural Network es_ES
dc.subject Commercial customers es_ES
dc.subject Customer flexibility es_ES
dc.subject Demand response programs es_ES
dc.subject Electrical consumption es_ES
dc.subject Electricity market es_ES
dc.subject End-uses es_ES
dc.subject Fundamental features es_ES
dc.subject High energy prices es_ES
dc.subject In-buildings es_ES
dc.subject Load curves es_ES
dc.subject Short term prediction es_ES
dc.subject Total power consumption es_ES
dc.subject Training data sets es_ES
dc.subject Customer satisfaction es_ES
dc.subject Electric load forecasting es_ES
dc.subject Energy resources es_ES
dc.subject Energy utilization es_ES
dc.subject Forecasting es_ES
dc.subject Sales es_ES
dc.subject Neural networks es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title New artificial neural network prediction method for electrical consumption forecasting based on building end-uses es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enbuild.2011.08.008
dc.relation.projectID info:eu-repo/grantAgreement/UPV//CE-19990032/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Ingeniería Energética - Institut d'Enginyeria Energètica es_ES
dc.description.bibliographicCitation Escrivá-Escrivá, G.; Álvarez Bel, CM.; Roldán Blay, C.; Alcázar-Ortega, M. (2011). New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy and Buildings. 43(11):3112-3119. https://doi.org/10.1016/j.enbuild.2011.08.008 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.enbuild.2011.08.008 es_ES
dc.description.upvformatpinicio 3112 es_ES
dc.description.upvformatpfin 3119 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 43 es_ES
dc.description.issue 11 es_ES
dc.relation.senia 200459
dc.contributor.funder Universitat Politècnica de València es_ES


This item appears in the following Collection(s)

Show simple item record