Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/14892
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dc.contributor.authorSilva, E. C. e.-
dc.contributor.authorBorges, A.-
dc.contributor.authorTeodoro, M. F.-
dc.contributor.authorAndrade, M. A. P.-
dc.contributor.authorCovas, R.-
dc.contributor.editorMadureira, A. M., Abraham, A., Gamboa, D., and Novais, P.-
dc.date.accessioned2018-01-08T18:03:59Z-
dc.date.available2018-01-08T18:03:59Z-
dc.date.issued2016-
dc.identifier.isbn978-3-319-53480-0-
dc.identifier.issn2194-5357-
dc.identifier.urihttp://hdl.handle.net/10071/14892-
dc.descriptionWOS:000406998500064 (Nº de Acesso Web of Science)-
dc.description.abstractRecently, at the 119th European Study Group with Industry, the Energy Solutions Operator EDP proposed a challenge concerning electricity prices simulation, not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. The main purpose was short-term Electricity Price Forecasting (EPF). This analysis is contextualized in the study of time series behavior, in particular multivariate time series, which is considered one of the current challenges in data mining. In this work a short-term EPF analysis making use of vector autoregressive models (VAR) with exogenous variables is proposed. The results show that the multivariate approach using VAR, with the season of the year and the type of day as exogenous variables, yield a model that explains the intra-day and intra-hour dynamics of the hourly prices.eng
dc.language.isoeng-
dc.publisherSpringer-
dc.relationUID/MULTI/0446/2013-
dc.rightsopenAccess-
dc.subjectData miningeng
dc.subjectElectricity prices forecastingeng
dc.subjectMultivariate time serieseng
dc.subjectVector autoregressive modelseng
dc.titleTime series data mining for energy prices forecasting: an application to real dataeng
dc.typeconferenceObject-
dc.event.title16th International Conference on Intelligent Systems Design and Applications (ISDA 2016)-
dc.event.typeConferênciapt
dc.event.locationPortoeng
dc.event.date2016-
dc.pagination649 - 658-
dc.publicationstatusPublicadopor
dc.peerreviewedyes-
dc.relation.publisherversionThe definitive version is available at: http://dx.doi.org/10.1007/978-3-319-53480-0_64por
dc.journalIntelligent Systems Design and Applications. Advances in Intelligent Systems and Computing-
dc.volume557-
degois.publication.firstPage649-
degois.publication.lastPage658-
degois.publication.locationPortoeng
degois.publication.titleTime series data mining for energy prices forecasting: an application to real dataeng
dc.date.updated2021-10-08T15:29:07Z-
dc.identifier.doi10.1007/978-3-319-53480-0_64-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.fosDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Civilpor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-43647-
iscte.alternateIdentifiers.wosWOS:000406998500064-
iscte.alternateIdentifiers.scopus2-s2.0-85014384507-
Aparece nas coleções:BRU-CRI - Comunicações a conferências internacionais

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