A hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.
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Data
2016
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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.
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Palavras-chave
Microgrid, Household electricity demand, Deep learning, Graphics processing
Citação
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.