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Load forecasting based on neural networks and load profiling

datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
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
dc.contributor.authorPires Neves, Luís
dc.contributor.authorH. M. Jorge
dc.date.accessioned2025-06-11T14:54:23Z
dc.date.available2025-06-11T14:54:23Z
dc.date.issued2009-06
dc.descriptionArticle number 5281832 - 2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future; Bucharest; Romania; 28 June 2009 through 2 July 2009; Category numberCFP09815-CDR; Code 78575
dc.description.abstractThis work presents a novel perspective of load forecasting based on neural networks and load profiling. In addition to the variables that are typically used to predict future load demand, such as past load values, meteorological variables, seasonal effects or macroeconomic indexes, it is expected that the use of load profiles and detailed information of individual consumers could favor the forecasting process. The methodology can be extended to different temporal horizons being predicted and the eventual threat of overparametrization is attenuated by the use of neural networks since the complexity of the model does not necessarily depends on the number of its weights and biases, as some of these parameters might be found irrelevant in the process. Another way to reduce the risk of overparametrization and overfitting is through the use of a considerable number of data points (whenever historical data is available) to train the network.eng
dc.identifier.citationJ. C. Sousa, L. P. Neves and H. M. Jorge, "Load forecasting based on neural networks and load profiling," 2009 IEEE Bucharest PowerTech, Bucharest, Romania, 2009, pp. 1-8, doi: https://doi.org/10.1109/PTC.2009.5281832.
dc.identifier.doi10.1109/ptc.2009.5281832
dc.identifier.isbn978-1-4244-2234-0
dc.identifier.isbn978-1-4244-2235-7EISBN
dc.identifier.urihttp://hdl.handle.net/10400.8/13200
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5281832
dc.relation.ispartof2009 IEEE Bucharest PowerTech
dc.rights.uriN/A
dc.subjectLoad Forecasting
dc.subjectLoad Profiling
dc.subjectNeural Networks
dc.titleLoad forecasting based on neural networks and load profilingeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2009-06
oaire.citation.conferencePlaceBucharest, Romania
oaire.citation.endPage8
oaire.citation.startPage1
oaire.citation.title2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcce
person.familyNameSousa
person.familyNamePires Neves
person.givenNameJoão
person.givenNameLuís
person.identifier.ciencia-id591E-30D4-2C97
person.identifier.orcid0000-0002-7567-4910
person.identifier.orcid0000-0002-2600-5622
person.identifier.scopus-author-id34977315800
relation.isAuthorOfPublication7678f744-5e50-4458-8811-33e1fbc63013
relation.isAuthorOfPublication5315d446-6d51-4d95-aeaa-72b6a44a4838
relation.isAuthorOfPublication.latestForDiscovery5315d446-6d51-4d95-aeaa-72b6a44a4838

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This work presents a novel perspective of load forecasting based on neural networks and load profiling. In addition to the variables that are typically used to predict future load demand, such as past load values, meteorological variables, seasonal effects or macroeconomic indexes, it is expected that the use of load profiles and detailed information of individual consumers could favor the forecasting process. The methodology can be extended to different temporal horizons being predicted and the eventual threat of overparametrization is attenuated by the use of neural networks since the complexity of the model does not necessarily depends on the number of its weights and biases, as some of these parameters might be found irrelevant in the process. Another way to reduce the risk of overparametrization and overfitting is through the use of a considerable number of data points (whenever historical data is available) to train the network.
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