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A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature

2012-11Journal article Open access
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dc.contributor.authorFerreira, Pedro M.
dc.contributor.authorGomes, João
dc.contributor.authorMartins, Igor A. C.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2018-12-07T14:57:58Z
dc.date.available2018-12-07T14:57:58Z
dc.date.issued2012-11
dc.description.abstractAccurate 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.
dc.description.sponsorshipPortuguese National Science and Technology Foundation [PTDC/ENR/73345/2006]; University of Algarve; European Commission [PERG-GA-2008-239451]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.3390/s121115750
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.1/11790
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI Ag
dc.relationIntelligent use of energy in public buildings
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectIterative selection method
dc.subjectModels
dc.subjectSystem
dc.subjectIrradiance
dc.subjectAlgorithm
dc.subjectImage
dc.subjectEnvironment
dc.subjectBuildings
dc.subjectIndexes
dc.subjectDesign
dc.titleA neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleIntelligent use of energy in public buildings
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FENR%2F73345%2F2006/PT
oaire.citation.endPage15777
oaire.citation.issue11
oaire.citation.startPage15750
oaire.citation.titleSensors
oaire.citation.volume12
oaire.fundingStream3599-PPCDT
person.familyNameFerreira
person.familyNameGomes
person.familyNameRuano
person.givenNamePedro
person.givenNameJoão
person.givenNameAntonio
person.identifier.ciencia-idE81C-C56F-D050
person.identifier.ciencia-id3112-8784-70FD
person.identifier.orcid0000-0003-2369-0115
person.identifier.orcid0000-0002-0346-6207
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id16425466700
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
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relation.isAuthorOfPublicatione7566cd7-4545-44bf-a5e1-4180967b997b
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscoverya81ee154-d26f-41c3-aaa7-90d5e639f2a0
relation.isProjectOfPublicatione19e74f6-3707-4811-b5d0-7cd11c238abf
relation.isProjectOfPublication.latestForDiscoverye19e74f6-3707-4811-b5d0-7cd11c238abf

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