Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9365
Title: Hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.
Authors: Coelho, Vitor Nazário
Coelho, Igor Machado
Rios, Eyder
Thiago Filho, Alexandre Magno de S.
Reis, Agnaldo José da Rocha
Coelho, Bruno Nazário
Alves, Alysson
Gaigher Netto, Guilherme
Souza, Marcone Jamilson Freitas
Guimarães, Frederico Gadelha
Keywords: Microgrid
Household electricity demand
Deep learning
Graphics processing
Issue Date: 2016
Citation: COELHO, V. N. et al. Hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting. Energy Procedia, v. 103, p. 280-285, 2016. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1876610216314965>. Acesso em: 16 jan. 2018.
Abstract: As 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.
URI: http://www.repositorio.ufop.br/handle/123456789/9365
ISSN: 18766102
metadata.dc.rights.license: This is an open access article under the CC BY-NC-ND license. Fonte: O próprio artigo.
Appears in Collections:DECOM - Artigos publicados em periódicos

Files in This Item:
File Description SizeFormat 
ARTIGO_HybridDeepLearning.pdf1,02 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.