Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs

Detalhes bibliográficos
Autor(a) principal: Pereira, Sara
Data de Publicação: 2018
Outros Autores: Canhoto, Paulo, Salgado, Rui, Costa, Maria João
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/37234
https://doi.org/10.24752/gre.1.0_43
Resumo: This paper presents a study on the influence of Sun-Earth geometry and atmospheric variables on the predictions of solar global irradiation (GHI) obtained from the ECMWF model. It was found that the differences between predictions and measurements of GHI are correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative humidity and total water column. An artificial neural network is developed to improve predictions of GHI for four locations being the base for a predicting algorithm that can be used in energy management models of solar systems thus allowing a better management of renewable energy conversion.
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spelling Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNssolar radiationsolar energysolar radiation forecastECMWF modelartificial neural networkThis paper presents a study on the influence of Sun-Earth geometry and atmospheric variables on the predictions of solar global irradiation (GHI) obtained from the ECMWF model. It was found that the differences between predictions and measurements of GHI are correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative humidity and total water column. An artificial neural network is developed to improve predictions of GHI for four locations being the base for a predicting algorithm that can be used in energy management models of solar systems thus allowing a better management of renewable energy conversion.Japan Council for Renewable Energy2024-08-27T14:45:12Z2024-08-272018-06-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/37234http://hdl.handle.net/10174/37234https://doi.org/10.24752/gre.1.0_43engPereira, S., Canhoto, P., Salgado, R., Costa, M. J. (2018). Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs. Grand Renewable Energy Proceedings, 2018 - International Conference and Exhibition, Japan Council for Renewable Energy, Vol. 1, pp. 43-46, 17-22 June 2018, Yokohama, Japan. ISSN: 2434-0871.spereira@uevora.ptcanhoto@uevora.ptrsal@uevora.ptmjcosta@uevora.pt286Pereira, SaraCanhoto, PauloSalgado, RuiCosta, Maria Joãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-09-10T01:48:21Zoai:dspace.uevora.pt:10174/37234Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-09-10T01:48:21Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
title Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
spellingShingle Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
Pereira, Sara
solar radiation
solar energy
solar radiation forecast
ECMWF model
artificial neural network
title_short Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
title_full Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
title_fullStr Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
title_full_unstemmed Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
title_sort Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
author Pereira, Sara
author_facet Pereira, Sara
Canhoto, Paulo
Salgado, Rui
Costa, Maria João
author_role author
author2 Canhoto, Paulo
Salgado, Rui
Costa, Maria João
author2_role author
author
author
dc.contributor.author.fl_str_mv Pereira, Sara
Canhoto, Paulo
Salgado, Rui
Costa, Maria João
dc.subject.por.fl_str_mv solar radiation
solar energy
solar radiation forecast
ECMWF model
artificial neural network
topic solar radiation
solar energy
solar radiation forecast
ECMWF model
artificial neural network
description This paper presents a study on the influence of Sun-Earth geometry and atmospheric variables on the predictions of solar global irradiation (GHI) obtained from the ECMWF model. It was found that the differences between predictions and measurements of GHI are correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative humidity and total water column. An artificial neural network is developed to improve predictions of GHI for four locations being the base for a predicting algorithm that can be used in energy management models of solar systems thus allowing a better management of renewable energy conversion.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-17T00:00:00Z
2024-08-27T14:45:12Z
2024-08-27
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/37234
http://hdl.handle.net/10174/37234
https://doi.org/10.24752/gre.1.0_43
url http://hdl.handle.net/10174/37234
https://doi.org/10.24752/gre.1.0_43
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Pereira, S., Canhoto, P., Salgado, R., Costa, M. J. (2018). Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs. Grand Renewable Energy Proceedings, 2018 - International Conference and Exhibition, Japan Council for Renewable Energy, Vol. 1, pp. 43-46, 17-22 June 2018, Yokohama, Japan. ISSN: 2434-0871.
spereira@uevora.pt
canhoto@uevora.pt
rsal@uevora.pt
mjcosta@uevora.pt
286
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Japan Council for Renewable Energy
publisher.none.fl_str_mv Japan Council for Renewable Energy
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
_version_ 1817547158748921856

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