Navegando por Autor "Rios, Eyder"

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A GPU deep learning metaheuristic based model for time series forecasting.
(2017) Coelho, Igor Machado; Coelho, Vitor Nazário; Luz, Eduardo José da Silva; Ochi, Luiz Satoru; Guimarães, Frederico Gadelha; Rios, Eyder
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) architecture provides a greener alternative with low energy consumption for mining big data, bringing the power of thousands of processing cores into a single chip, thus opening a wide range of possible applications. In this paper (a substantial extension of the short version presented at REM2016 on April 19–21, Maldives [1]), we design a novel parallel strategy for time series learning, in which different parts of the time series are evaluated by different threads. The proposed strategy is inserted inside the core a hybrid metaheuristic model, applied for learning patterns from an important mini/microgrid forecasting problem, the household electricity demand forecasting. The future smart cities will surely rely on distributed energy generation, in which citizens should be aware about how to manage and control their own resources. In this sense, energy disaggregation research will be part of several typical and useful microgrid applications. Computational results show that the proposed GPU learning strategy is scalable as the number of training rounds increases, emerging as a promising deep learning tool to be embedded into smart sensors.
A hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.
(2016) 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
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.