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Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model

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Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model

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dc.contributor.author Roldán Blay, Carlos es_ES
dc.contributor.author Escrivá-Escrivá, Guillermo es_ES
dc.contributor.author Álvarez Bel, Carlos María es_ES
dc.contributor.author Roldán Porta, Carlos es_ES
dc.contributor.author Rodriguez-Garcia, Javier es_ES
dc.date.accessioned 2014-12-04T11:19:59Z
dc.date.available 2014-12-04T11:19:59Z
dc.date.issued 2013-05
dc.identifier.issn 0378-7788
dc.identifier.uri http://hdl.handle.net/10251/45164
dc.description.abstract This paper presents the upgrading of a method for predicting short-term building energy consumption that was previously developed by the authors (EUs method). The upgrade uses a time temperature curve (TTC) forecast model. The EUs method involves the use of artificial neural networks (ANNs) for predicting each independent process end-uses (EUs). End-uses consume energy with a specific behaviour in function of certain external variables. The EUs method obtains the total consumption by the addition of the forecasted end-uses. The inputs required for this method are the parameters that may affect consumption, such as temperature, type of day, etc. Historical data of the total consumption and the consumption of each end-use are also required. A model for prediction of the time temperature curve has been developed for the new forecast method (TEUs method). The temperature at each moment of the day is obtained using the prediction of the maximum and minimum daytime temperature. This provides various benefits when selecting the training days and in the training and forecasting phases, thus improving the relationship between expected consumption and temperatures. The method has been tested and validated with the consumption forecast of the Universitat Politècnica de València for an entire year. 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 Universitat Politecnica de Valencia (Spain) CE 19990032 es_ES
dc.relation.ispartof Energy and Buildings es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Temperature curve model es_ES
dc.subject Building energy consumption forecast es_ES
dc.subject Artificial neural networks es_ES
dc.subject Building end-uses es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enbuild.2012.12.009
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 Roldán Blay, C.; Escrivá-Escrivá, G.; Álvarez Bel, CM.; Roldán Porta, C.; Rodriguez-Garcia, J. (2013). Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model. Energy and Buildings. 60:38-46. doi:10.1016/j.enbuild.2012.12.009 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.enbuild.2012.12.009 es_ES
dc.description.upvformatpinicio 38 es_ES
dc.description.upvformatpfin 46 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 60 es_ES
dc.relation.senia 233759


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