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Modeling the variability of solar radiation data among weather stations by means of principal components analysis

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Modeling the variability of solar radiation data among weather stations by means of principal components analysis

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dc.contributor.author Zarzo Castelló, Manuel es_ES
dc.contributor.author Martí Pérez, Pau Carles es_ES
dc.date.accessioned 2014-10-06T07:41:23Z
dc.date.available 2014-10-06T07:41:23Z
dc.date.issued 2011-08
dc.identifier.issn 0306-2619
dc.identifier.uri http://hdl.handle.net/10251/40653
dc.description.abstract Measurements of global terrestrial solar radiation (R s) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches considered. The proposed methodology for estimating R s based on geographical parameters would be of interest to design solar energy systems and to select their best location. © 2011 Elsevier Ltd. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Applied Energy es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Missing data estimation es_ES
dc.subject Multivariate statistical monitoring es_ES
dc.subject PCA es_ES
dc.subject Solar radiation es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Modeling the variability of solar radiation data among weather stations by means of principal components analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.apenergy.2011.01.070
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Zarzo Castelló, M.; Martí Pérez, PC. (2011). Modeling the variability of solar radiation data among weather stations by means of principal components analysis. Applied Energy. 88(8):2775-2784. doi:10.1016/j.apenergy.2011.01.070 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.apenergy.2011.01.070 es_ES
dc.description.upvformatpinicio 2775 es_ES
dc.description.upvformatpfin 2784 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 88 es_ES
dc.description.issue 8 es_ES
dc.relation.senia 41297


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