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Short-term load forecasting based on support vector regression and load profiling

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorSousa, João C.
dc.contributor.authorJorge, Humberto M.
dc.contributor.authorNeves, Luís P.
dc.date.accessioned2026-07-20T14:12:17Z
dc.date.available2026-07-20T14:12:17Z
dc.date.issued2014
dc.description.abstractThe article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance.eng
dc.description.sponsorshipThis work has been partially supported by FCT through projects PEst-C/EEI/UI0308/2011 and MIT/SET/0018/2009.
dc.identifier.citationSousa, J.C., Jorge, H.M. and Neves, L.P. (2014), Short-term load forecasting based on support vector regression and load profiling. Int. J. Energy Res., 38: 350-362. https://doi.org/10.1002/er.3048
dc.identifier.doi10.1002/er.3048
dc.identifier.issn0363-907X
dc.identifier.urihttp://hdl.handle.net/10400.8/16622
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWiley
dc.relationStrategic Project - UI 308 - 2011-2012
dc.relationEnergy Box - development and implementation of a demand-responsive energy management system
dc.relation.hasversionhttps://onlinelibrary.wiley.com/doi/full/10.1002/er.3048?msockid=0141d3ca0649600f210cc49807cd61f6
dc.relation.ispartofInternational Journal of Energy Research
dc.rights.uriN/A
dc.titleShort-term load forecasting based on support vector regression and load profilingeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberPEst-C/EEI/UI0308/2011
oaire.awardNumberMIT/SET/0018/2009
oaire.awardTitleStrategic Project - UI 308 - 2011-2012
oaire.awardTitleEnergy Box - development and implementation of a demand-responsive energy management system
oaire.awardURIhttp://hdl.handle.net/10400.8/16292
oaire.awardURIhttp://hdl.handle.net/10400.8/16291
oaire.citation.endPage362
oaire.citation.issue3
oaire.citation.startPage350
oaire.citation.titleInternational Journal of Energy Research
oaire.citation.volume38
oaire.fundingStreamConcurso para Financiamento de Projectos de IC&DT Estratégicos e de Interesse Público promovidos por Laboratórios Associados e Unidades de I&D - 2011 - COMPETE
oaire.fundingStreamConcurso de Projectos de Investigação Científica e Desenvolvimento Tecnológico no Âmbito do Acordo de Cooperação entre Portugal e o MIT - 2009
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePires Neves
person.givenNameLuís
person.identifier.ciencia-id591E-30D4-2C97
person.identifier.orcid0000-0002-2600-5622
person.identifier.scopus-author-id34977315800
relation.isAuthorOfPublication5315d446-6d51-4d95-aeaa-72b6a44a4838
relation.isAuthorOfPublication.latestForDiscovery5315d446-6d51-4d95-aeaa-72b6a44a4838
relation.isProjectOfPublicationac27d8b7-a6bd-4601-8f85-a26c2108fac0
relation.isProjectOfPublication047875a5-39ef-407a-ac0f-d0b642406b7b
relation.isProjectOfPublication.latestForDiscoveryac27d8b7-a6bd-4601-8f85-a26c2108fac0

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