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Short-term load forecasting using information obtained from low voltage load profiles

2009-03Conference paper Restricted access
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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
dc.contributor.authorPires Neves, Luís
dc.contributor.authorHumberto M.M. Jorge
dc.date.accessioned2025-06-06T16:10:31Z
dc.date.available2025-06-06T16:10:31Z
dc.date.issued2009-03
dc.descriptionArticle number 4915229, 2nd International Conference on Power Engineering, Energy and Electrical Drives, POWERENG 2009, 18 March 2009 through 20 March 2009 - Code 77128
dc.description.abstractRecent researches in load forecasting are quite often based on the use of neural networks in order to predict a specific variable (maximum demand, active electric power or hourly consumption) using past values of the same variable and other exogenous factors proved to influence the value being predicted. This work aims to explore different input patterns in neural networks incorporating information derived from load profiles of different consumers' classes.eng
dc.identifier.citationJ. M. C. Sousa, L. M. P. Neves and H. M. M. Jorge, "Short-term load forecasting using information obtained from low voltage load profiles," 2009 International Conference on Power Engineering, Energy and Electrical Drives, Lisbon, Portugal, 2009, pp. 655-660, doi: https://doi.org/10.1109/POWERENG.2009.4915229.
dc.identifier.doi10.1109/powereng.2009.4915229
dc.identifier.eissn2155-5532
dc.identifier.isbn978-1-4244-2290-6
dc.identifier.issn2155-5516
dc.identifier.urihttp://hdl.handle.net/10400.8/13181
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/4915229
dc.relation.ispartof2009 International Conference on Power Engineering, Energy and Electrical Drives
dc.rights.uriN/A
dc.subjectLoad forecasting
dc.subjectLow voltage
dc.subjectNeural networks
dc.subjectTechnology management
dc.subjectAutocorrelation
dc.subjectWeather forecasting
dc.subjectArtificial neural networks
dc.subjectPredictive models
dc.subjectComputer networks
dc.subjectResearch and development
dc.titleShort-term load forecasting using information obtained from low voltage load profileseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2009-03
oaire.citation.conferencePlaceLisbon, Portugal
oaire.citation.endPage660
oaire.citation.startPage655
oaire.citation.titleInternational Conference on Power Engineering, Energy and Electrical Drives
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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.latestForDiscovery7678f744-5e50-4458-8811-33e1fbc63013

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Recent researches in load forecasting are quite often based on the use of neural networks in order to predict a specific variable (maximum demand, active electric power or hourly consumption) using past values of the same variable and other exogenous factors proved to influence the value being predicted. This work aims to explore different input patterns in neural networks incorporating information derived from load profiles of different consumers' classes.
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