Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices

Bibliographic Details
Main Author: Ruano, Antonio
Publication Date: 2008
Other Authors: Crispim, E. M., Ferreira, P. M.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.1/2224
Summary: In this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as onestep- ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera.
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spelling Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indicesCloudiness indicesSolar radiationNeural networksMulti-objective genetic algorithmsIn this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as onestep- ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera.SapientiaRuano, AntonioCrispim, E. M.Ferreira, P. M.2013-02-05T15:00:04Z20082013-01-26T18:02:24Z2008-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/2224eng1349-4198AUT: ARU00698;info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-18T17:48:35Zoai:sapientia.ualg.pt:10400.1/2224Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:36:54.230991Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
title Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
spellingShingle Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
Ruano, Antonio
Cloudiness indices
Solar radiation
Neural networks
Multi-objective genetic algorithms
title_short Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
title_full Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
title_fullStr Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
title_full_unstemmed Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
title_sort Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices
author Ruano, Antonio
author_facet Ruano, Antonio
Crispim, E. M.
Ferreira, P. M.
author_role author
author2 Crispim, E. M.
Ferreira, P. M.
author2_role author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Ruano, Antonio
Crispim, E. M.
Ferreira, P. M.
dc.subject.por.fl_str_mv Cloudiness indices
Solar radiation
Neural networks
Multi-objective genetic algorithms
topic Cloudiness indices
Solar radiation
Neural networks
Multi-objective genetic algorithms
description In this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as onestep- ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera.
publishDate 2008
dc.date.none.fl_str_mv 2008
2008-01-01T00:00:00Z
2013-02-05T15:00:04Z
2013-01-26T18:02:24Z
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/10400.1/2224
url http://hdl.handle.net/10400.1/2224
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1349-4198
AUT: ARU00698;
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.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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