A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/11790 |
Resumo: | Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. |
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A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperatureIterative selection methodModelsSystemIrradianceAlgorithmImageEnvironmentBuildingsIndexesDesignAccurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.Portuguese National Science and Technology Foundation [PTDC/ENR/73345/2006]; University of Algarve; European Commission [PERG-GA-2008-239451]MDPI AgSapientiaFerreira, Pedro M.Gomes, JoãoMartins, Igor A. C.Ruano, Antonio2018-12-07T14:57:58Z2012-112012-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/11790eng1424-822010.3390/s121115750info: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:RCAAP2023-07-24T10:23:39Zoai:sapientia.ualg.pt:10400.1/11790Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:03:14.743067Repositório 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 |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
title |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
spellingShingle |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature Ferreira, Pedro M. Iterative selection method Models System Irradiance Algorithm Image Environment Buildings Indexes Design |
title_short |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
title_full |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
title_fullStr |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
title_full_unstemmed |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
title_sort |
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature |
author |
Ferreira, Pedro M. |
author_facet |
Ferreira, Pedro M. Gomes, João Martins, Igor A. C. Ruano, Antonio |
author_role |
author |
author2 |
Gomes, João Martins, Igor A. C. Ruano, Antonio |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Ferreira, Pedro M. Gomes, João Martins, Igor A. C. Ruano, Antonio |
dc.subject.por.fl_str_mv |
Iterative selection method Models System Irradiance Algorithm Image Environment Buildings Indexes Design |
topic |
Iterative selection method Models System Irradiance Algorithm Image Environment Buildings Indexes Design |
description |
Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-11 2012-11-01T00:00:00Z 2018-12-07T14:57:58Z |
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/10400.1/11790 |
url |
http://hdl.handle.net/10400.1/11790 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-8220 10.3390/s121115750 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI Ag |
publisher.none.fl_str_mv |
MDPI Ag |
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 |
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1799133267047219200 |