Use este identificador para citar ou linkar para este item:
http://www.repositorio.ufop.br/jspui/handle/123456789/9365
Título: | A hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting. |
Autor(es): | 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 |
Palavras-chave: | Microgrid Household electricity demand Deep learning Graphics processing |
Data do documento: | 2016 |
Referência: | COELHO, V. N. et al. A 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. |
Resumo: | 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 |
DOI: | https://doi.org/10.1016/j.egypro.2016.11.286 |
ISSN: | 1876-6102 |
Licença: | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Fonte: o próprio artigo. |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
---|---|---|---|---|
ARTIGO_HybridDeepLearning.pdf | 1,02 MB | Adobe PDF | Visualizar/Abrir |
Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.