Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks

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dc.citation.volume87
dc.contributor.authorLima, Francisco José Lopes de
dc.contributor.authorMartins, Fernando Ramos [UNIFESP]
dc.contributor.authorPereira, Enio Bueno
dc.contributor.authorLorenz, Elke
dc.contributor.authorHeinemann, Detlev [UNIFESP]
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.coverageOxford
dc.date.accessioned2020-08-21T17:00:21Z
dc.date.available2020-08-21T17:00:21Z
dc.date.issued2016
dc.description.abstractThere has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is establishing a methodology to produce solar irradiation forecasts for the Brazilian Northeastern region by using Weather Research and Forecasting Model (WRF) combined with a statistical post-processing method. The 24 h solar irradiance forecasts were obtained using the WRF model. In order to reduce uncertainties, a cluster analysis technique was employed to select areas presenting similar climate features. Comparison analysis between VVRF model outputs and observational data were performed to evaluate the model skill in forecasting surface solar irradiance. Next, model-derived short-term solar irradiance forecasts from the WRF outputs were refined by using an artificial neural networks (ANNs) technique. The output variables of the WRF model representing the forecasted atmospheric conditions were used as predictors by ANNs, adjusted to calculate the solar radiation incident for the entire Brazilian Northeastern (NEB) (which was divided into four homogeneous regions, defined by the Ward method). The data used in this study was from rainy and dry seasons between 2009 and 2011. Several predictors were tested to adjust and simulate the ANNs. We found the best ANN architecture and a group of 10 predictors, in which a deeper analyzes were carried out, including performance evaluation for Fall and Spring of 2011 (rainy and dry season in NEB, mainly in the northern section). There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient. (C) 2015 Elsevier Ltd. All rights reserved.en
dc.description.affiliationINPE Brazilian Natl Inst Space Res, CPTEC Ctr Weather Forecasting & Climate Res, Sao Jose Dos Campos, Brazil
dc.description.affiliationUniv Fed Sao Paulo, UNIFESP, Santos, Brazil
dc.description.affiliationCarl von Ossietzky Univ Oldenburg, Inst Phys, Energy Meteorol Unit, D-26111 Oldenburg, Germany
dc.description.affiliationUnifespUniv Fed Sao Paulo, UNIFESP, Santos, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)/DAAD/GIZ/NOPApt
dc.description.sponsorshipIDCNPq: 142083/2012-7
dc.description.sponsorshipIDCAPES/DAAD/GIZ/NOPA: 4404/13-0
dc.format.extent807-818
dc.identifierhttps://dx.doi.org/10.1016/j.renene.2015.11.005
dc.identifier.citationRenewable Energy. Oxford, v. 87, p. 807-818, 2016.
dc.identifier.doi10.1016/j.renene.2015.11.005
dc.identifier.issn0960-1481
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/57949
dc.identifier.wosWOS:000367759500079
dc.language.isoeng
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofRenewable Energy
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectSolar energy forecasten
dc.subjectArtificial neural networken
dc.subjectWRF modelen
dc.subjectSolar irradianceen
dc.titleForecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networksen
dc.typeinfo:eu-repo/semantics/article
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