An Intelligent Weather Station

Detalhes bibliográficos
Autor(a) principal: Mestre, Goncalo
Data de Publicação: 2015
Outros Autores: Ruano, Antonio, Duarte, Helder, Silva, Sergio, Khosravani, Hamid Reza, Pesteh, Shabnam, Ferreira, Pedro M., Horta, Ricardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/11204
Resumo: Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, 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, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.
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spelling An Intelligent Weather StationNeural-network modelsThermal comfortSolar-radiationAir-temperatureParametersAlgorithmAccurate measurements of global solar radiation, atmospheric temperature and relative humidity, 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, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.QREN SIDT [38798]; University of Algarve [032/2015]MDPISapientiaMestre, GoncaloRuano, AntonioDuarte, HelderSilva, SergioKhosravani, Hamid RezaPesteh, ShabnamFerreira, Pedro M.Horta, Ricardo2018-12-07T14:52:46Z2015-122015-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/11204eng1424-822010.3390/s151229841info: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:RCAAP2023-07-24T10:22:58ZPortal AgregadorONG
dc.title.none.fl_str_mv An Intelligent Weather Station
title An Intelligent Weather Station
spellingShingle An Intelligent Weather Station
Mestre, Goncalo
Neural-network models
Thermal comfort
Solar-radiation
Air-temperature
Parameters
Algorithm
title_short An Intelligent Weather Station
title_full An Intelligent Weather Station
title_fullStr An Intelligent Weather Station
title_full_unstemmed An Intelligent Weather Station
title_sort An Intelligent Weather Station
author Mestre, Goncalo
author_facet Mestre, Goncalo
Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid Reza
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
author_role author
author2 Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid Reza
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Mestre, Goncalo
Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid Reza
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
dc.subject.por.fl_str_mv Neural-network models
Thermal comfort
Solar-radiation
Air-temperature
Parameters
Algorithm
topic Neural-network models
Thermal comfort
Solar-radiation
Air-temperature
Parameters
Algorithm
description Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, 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, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
2015-12-01T00:00:00Z
2018-12-07T14:52:46Z
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/10400.1/11204
url http://hdl.handle.net/10400.1/11204
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
10.3390/s151229841
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
publisher.none.fl_str_mv MDPI
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
repository.mail.fl_str_mv
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