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A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zones

2024-06Journal 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.contributor.authorRabehi, Abdelaziz
dc.date.accessioned2024-09-17T08:59:30Z
dc.date.available2024-09-17T08:59:30Z
dc.date.issued2024-06
dc.description.abstractThe increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar irradiance based on an innovative evolutionary forest approach. Our model, baptized EFITS, performs incremental tree selection through appropriate evolutionary operators maintaining a good tradeoff between accuracy and diversity, generating progressively near-optimal decision trees to construct the final evolutionary forest forecaster. This new evolution process also automatically selects near-optimal input parameters, enhancing the overall model accuracy and generalization ability. Six climatically diverse locations in Morocco and three types of inputs (endogenous, exogenous, and hybrid) are used to assess the performance of the proposed. The results demonstrate that our proposed model exhibits excellent performance across all studied sites and horizons. Among all input types, hybrid inputs delivered the best forecasting accuracy across all studied sites and horizons. Notably, the continental climate site (Bni Mellal) achieved the highest accuracy, with nRMSE ranging from 4.94% to 7.54% and nMBE from 0.71% to -0.46% for 1 to 6 h forecasts. Conversely, Ifrane city, characterized by a humid temperate climate, showed the lowest accuracy, with nRMSE ranging from 10.34% to 18.94% and nMBE from 1.21% to -1.54%. Finally, a detailed comparison with benchmarking models (random forest, bagging, gradient boosting, single decision tree, bidirectional long short-term memory network, and scaled persistence models), revealed that our model consistently outperforms them across all tested scenarios, locations, and forecasting horizons.eng
dc.identifier.doi10.1016/j.enconman.2024.118471
dc.identifier.issn0196-8904
dc.identifier.urihttp://hdl.handle.net/10400.1/25894
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relation.ispartofEnergy Conversion and Management
dc.rights.uriN/A
dc.subjectGlobal solar irradiance
dc.subjectEvolutionary forest model
dc.subjectIncremental tree selection
dc.subjectShort-term forecasting
dc.subjectHybrid inputs
dc.subjectClimatic zones
dc.titleA new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zoneseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50022%2F2020/PT
oaire.citation.startPage118471
oaire.citation.titleEnergy Conversion and Management
oaire.citation.volume310
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublicationc455c151-2f71-4492-b5ed-9231048a9dca
relation.isProjectOfPublication.latestForDiscoveryc455c151-2f71-4492-b5ed-9231048a9dca

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