A hybrid deep learning forecasting model using gpu disaggregated function evaluations applied for household electricity demand forecasting

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dc.creatorVitor Nazário Coelho
dc.creatorIgor Machado Coelho
dc.creatorEyder Rios
dc.creatorAlexandre Filho
dc.creatorAgnaldo Rocha Reis
dc.creatorBruno Nazário Coelho
dc.creatorAlysson Alves
dc.creatorGuilherme Netto
dc.creatorMarcone Jamilson Freitas Souza
dc.creatorFrederico Gadelha Guimarães
dc.date.accessioned2023-07-24T21:12:56Z
dc.date.accessioned2025-09-08T23:07:16Z
dc.date.available2023-07-24T21:12:56Z
dc.date.issued2016
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1016/j.egypro.2016.11.286
dc.identifier.issn1876-6102
dc.identifier.urihttps://hdl.handle.net/1843/56921
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofEnergy Procedia
dc.rightsAcesso Aberto
dc.subjectEngenharia elétrica
dc.subjectEngenharia elétrica - Materiais
dc.subject.otherMicrogrid
dc.subject.otherHousehold Electricity Demand
dc.subject.otherDeep Learning
dc.subject.otherGraphics Processing Unit
dc.subject.otherParallel forecasting model
dc.subject.otherBig Time-series Data
dc.titleA hybrid deep learning forecasting model using gpu disaggregated function evaluations applied for household electricity demand forecasting
dc.typeArtigo de periódico
local.citation.epage285
local.citation.spage280
local.citation.volume103
local.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.
local.identifier.orcidhttps://orcid.org/0000-0001-9238-8839
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
local.publisher.departmentENGENHARIA - ESCOLA DE ENGENHARIA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S1876610216314965?via%3Dihub

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