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Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance

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Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance

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dc.contributor.author Martí Pérez, Pau Carles es_ES
dc.contributor.author Gasque Albalate, Maria es_ES
dc.date.accessioned 2017-04-07T12:31:03Z
dc.date.available 2017-04-07T12:31:03Z
dc.date.issued 2011-02
dc.identifier.issn 0196-8904
dc.identifier.uri http://hdl.handle.net/10251/79567
dc.description.abstract [EN] The development of new and more precise temperature-based models for solar radiation estimation is decisive, given the immediacy and simplicity associated to their input measurements and the ubiquitous problems derived from equipment failures, maintenance and calibration, and physical and biological constraints. Further, the performance quality of empirical equations is to be questioned in a large variety of climatic contexts. As an alternative to traditional techniques, artificial neural networks (ANNs) are highly appropriate for the modelling of non-linear processes. Nevertheless, temperature-based ANN models do not always provide accurate enough solar radiation estimations as their performance depends considerably on the specific temperature/solar radiation relationships of the studied context. This paper describes a new procedure to improve the performance accuracy of temperature-based ANN models for estimation of total solar radiation on a horizontal surface (Rs) taking advantage of ancillary data records from secondary similar stations, which work as exogenous inputs. The influence on the model performance of the number of considered ancillary stations and the corresponding number of training patterns is also analyzed. Finally, these models are compared with those relying exclusively on local temperature recordings. The proposed models provide performances with lower associated errors than those which do not consider exogenous inputs. The ancillary supply is translated into a decrease around 0.1 of RMSE in the local performance. The consideration of non-measured inputs in the simple local temperature-based models, namely extraterrestrial radiation or day of the year, entails a performance accuracy improvement around 0.1 of RMSE. © 2010 Elsevier Ltd. All rights reserved. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Energy Conversion and Management es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial neural networks es_ES
dc.subject Exogenous variables es_ES
dc.subject Solar radiation es_ES
dc.subject Accuracy Improvement es_ES
dc.subject Ancillary data es_ES
dc.subject Artificial Neural Network es_ES
dc.subject Empirical equations es_ES
dc.subject Equipment failures es_ES
dc.subject Exogenous input es_ES
dc.subject Horizontal surfaces es_ES
dc.subject Input measurements es_ES
dc.subject Local temperature es_ES
dc.subject Model performance es_ES
dc.subject Nonlinear process es_ES
dc.subject Performance quality es_ES
dc.subject Solar radiation estimation es_ES
dc.subject Traditional techniques es_ES
dc.subject Training patterns es_ES
dc.subject Estimation es_ES
dc.subject Models es_ES
dc.subject Sun es_ES
dc.subject Neural networks es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enconman.2010.08.027
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Rural y Agroalimentaria - Departament d'Enginyeria Rural i Agroalimentària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Centro Valenciano de Estudios sobre el Riego - Centre Valencià d'Estudis sobre el Reg es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.description.bibliographicCitation Martí Pérez, PC.; Gasque Albalate, M. (2011). Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance. Energy Conversion and Management. 52(2):990-1003. doi:10.1016/j.enconman.2010.08.027 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.enconman.2010.08.027 es_ES
dc.description.upvformatpinicio 990 es_ES
dc.description.upvformatpfin 1003 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 52 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 39587 es_ES


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