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A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends

2022Journal article Restricted access
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dc.contributor.authorEl-Amarty, Naima
dc.contributor.authorMarzouq, Manal
dc.contributor.authorEl Fadili, Hakim
dc.contributor.authorBennani, Saad Dosse
dc.contributor.authorRuano, Antonio
dc.date.accessioned2023-02-09T09:30:02Z
dc.date.available2023-02-09T09:30:02Z
dc.date.issued2022
dc.description.abstractSolar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/s11356-022-24240-wpt_PT
dc.identifier.issn0944-1344
dc.identifier.urihttp://hdl.handle.net/10400.1/19029
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.subjectSolar irradiationpt_PT
dc.subjectClimate conditionpt_PT
dc.subjectFeature selectionpt_PT
dc.subjectANN modelpt_PT
dc.subjectForecasting horizonpt_PT
dc.subjectDeep learningpt_PT
dc.titleA comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trendspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.endPage5439pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage5407pt_PT
oaire.citation.titleEnvironmental Science and Pollution Researchpt_PT
oaire.citation.volume30pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublication9df77b70-8231-47e7-9b34-c702e9c6021c
relation.isProjectOfPublication.latestForDiscovery9df77b70-8231-47e7-9b34-c702e9c6021c

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