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http://hdl.handle.net/10071/20235
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dc.contributor.advisorNunes, Luís Miguel Martins-
dc.contributor.advisorSilva, Nuno Pinho da-
dc.contributor.authorJanuário, João Filipe Ferreira-
dc.date.accessioned2020-03-27T11:04:02Z-
dc.date.available2020-03-27T11:04:02Z-
dc.date.issued2019-11-04-
dc.date.submitted2019-01-
dc.identifier.citationJANUÁRIO, João Filipe Ferreira - Electricity price forecasting utilizing machine learning in MIBEL [Em linha]. Lisboa: ISCTE-IUL, 2019. Dissertação de mestrado. [Consult. Dia Mês Ano] Disponível em www:<http://hdl.handle.net/10071/20235>.pt-PT
dc.identifier.urihttp://hdl.handle.net/10071/20235-
dc.description.abstractShort term electricity price forecasts have become increasingly important in the last few decades due to the rise of more competitive electricity markets throughout the globe. Accurate forecasts are now essential for market players to maximize their profits and hedge against risk, hence various forecasting methodologies have been applied to electricity price forecasting in the last few decades. This dissertation explores the main methodologies and how accurately can three popular machine learning models, SVR LSTM and XGBoost, predict prices in the Iberian market of electricity. Additionally, a study on input variables and their relationship with the final price is made.por
dc.language.isoengpor
dc.rightsopenAccesspor
dc.subjectMachine learningpor
dc.subjectElectricitypor
dc.subjectClearing marketpor
dc.subjectPredictionpor
dc.subjectInput variablespor
dc.subjectEletricidade-
dc.subjectPreços-
dc.subjectMétodos de previsão-
dc.titleElectricity price forecasting utilizing machine learning in MIBELpor
dc.typemasterThesispor
dc.peerreviewedyespor
dc.identifier.tid202460177por
dc.subject.fosDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
thesis.degree.nameMestrado em Engenharia Informáticapor
Appears in Collections:T&D-DM - Dissertações de mestrado

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