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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/56921
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dc.creatorVitor N. Coelhopt_BR
dc.creatorFrederico Gadelha Guimarãespt_BR
dc.creatorIgor M. Coelhopt_BR
dc.creatorEyder Riospt_BR
dc.creatorAlexandre S. T. Filhopt_BR
dc.creatorAgnaldo J. R. Reispt_BR
dc.creatorBruno N. Coelhopt_BR
dc.creatorAlysson Alvespt_BR
dc.creatorGuilherme G. Nettopt_BR
dc.creatorMarcone J. F. Souzapt_BR
dc.date.accessioned2023-07-24T21:12:56Z-
dc.date.available2023-07-24T21:12:56Z-
dc.date.issued2016-
dc.citation.volume103pt_BR
dc.citation.spage280pt_BR
dc.citation.epage285pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.egypro.2016.11.286pt_BR
dc.identifier.issn1876-6102pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/56921-
dc.description.resumoAs the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases.pt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICApt_BR
dc.publisher.departmentENGENHARIA - ESCOLA DE ENGENHARIApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofEnergy Procediapt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectMicrogridpt_BR
dc.subjectHousehold Electricity Demandpt_BR
dc.subjectDeep Learningpt_BR
dc.subjectGraphics Processing Unitpt_BR
dc.subjectParallel forecasting modelpt_BR
dc.subjectBig Time-series Datapt_BR
dc.subject.otherEngenharia elétricapt_BR
dc.subject.otherEngenharia elétrica - Materiaispt_BR
dc.titleA hybrid deep learning forecasting model using gpu disaggregated function evaluations applied for household electricity demand forecastingpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S1876610216314965?via%3Dihubpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9238-8839pt_BR
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