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dc.contributor.author
Wang, Lunche
dc.contributor.author
Kisi, Ozgur
dc.contributor.author
Zounemat Kermani, Mohammad
dc.contributor.author
Salazar, Germán Ariel
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dc.contributor.author
Zhu, Zhongmin
dc.contributor.author
Gong, Wei
dc.date.available
2018-02-19T20:33:36Z
dc.date.issued
2016-08
dc.identifier.citation
Wang, Lunche; Kisi, Ozgur; Zounemat Kermani, Mohammad; Salazar, Germán Ariel; Zhu, Zhongmin; et al.; Solar radiation prediction using different techniques: model evaluation and comparison; Pergamon-Elsevier Science Ltd.; Renewable & Sustainable Energy Reviews; 61; 8-2016; 384-397
dc.identifier.issn
1364-0321
dc.identifier.uri
http://hdl.handle.net/11336/36778
dc.description.abstract
Daily observations of meteorological parameters, air temperature, air pressure, relative humidity, water vapor pressure and sunshine duration hours observed at 12 stations in different climatic zones during 1961-2014 are reported for testing, validating and comparing different solar radiation models. Three types of Artificial Neural Network (ANN)methods, Multilayer Perceptron (MLP), Generalized Regression Neural Network (GRNN) and Radial Basis Neural Network (RBNN) are applied in this study for predicting the daily global solar radiation (Hg) using above meteorological variables as model inputs. The Bristow-Campbell model has also been improved by considering the factors influencing the incoming solar radiation, such as relative humidity, cloud cover, etc. The results indicate that there are large differences in model accuracies for each model at different stations, the ANN models can estimate daily Hg with satisfactory accuracy at most stations in different climate zones, and MLP and RBNN models provide better accuracy than the GRNN and IBC models, for example, the MAE and RMSE values range 1.53-2.29 and 1.94-3.27 MJ m-2 day-1, respectively for MLP model. The model performances also show some differences at different stations for each model, for example, the RMSE values from MLP model are 1.94 and 3.27 MJ m-2 day-1at NN and HZ stations, respectively. Meanwhile, ANN models underestimate few high radiation values at some stations, which may due to the differences in training and testing data ranges and distributions of the stations. Finally, the differences in model performances from different solar radiation models have been further analyzed.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd.
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Solar Radiation
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Generalized Regression Neural Network
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Multilayer Perceptron
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Radial Basis Neural Network
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Improved Bristow-Campbell Model
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Model Evaluation
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Solar radiation prediction using different techniques: model evaluation and comparison
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-02-19T16:55:24Z
dc.journal.volume
61
dc.journal.pagination
384-397
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Wang, Lunche. China University Of Geosciences, Wuhan; China
dc.description.fil
Fil: Kisi, Ozgur. Canik Basari Universitesi; Turquía
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Fil: Zounemat Kermani, Mohammad. Shahid Bahonar University Of Kerman; Irán
dc.description.fil
Fil: Salazar, Germán Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina
dc.description.fil
Fil: Zhu, Zhongmin. Huazhong University Of Science And Technology; China. Wuhan University; China
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Fil: Gong, Wei. Wuhan University; China. Collaborative Innovation Center Of Geospatial Technology; China
dc.journal.title
Renewable & Sustainable Energy Reviews
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.rser.2016.04.024
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