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Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns

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dc.contributor.authorVinagre, Eugénia
dc.contributor.authorPaz, Juan F. De
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorCorchado, Juan M.
dc.contributor.authorGarcia, Oscar
dc.date.accessioned2017-07-07T11:26:01Z
dc.date.embargo2117
dc.date.issued2016
dc.description.abstractThe increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation – campus of the Polytechnic of Porto, in real time.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/SSCI.2016.7849853pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9996
dc.language.isoengpt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.relation.ispartofseriesSSCI;2016
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7849853/pt_PT
dc.subjectArtificial Neural Networkpt_PT
dc.subjectElectricity Consumptionpt_PT
dc.subjectSolar Radiationpt_PT
dc.subjectSupport Vector Regressionpt_PT
dc.titleIntelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patternspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titlePROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCEpt_PT
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery8d58ddc0-1023-47c0-a005-129d412ce98d

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