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

Coelho, Vitor NazárioCoelho, Igor MachadoRios, EyderThiago Filho, Alexandre Magno de S.Reis, Agnaldo José da RochaCoelho, Bruno NazárioAlves, AlyssonGaigher Netto, GuilhermeSouza, Marcone Jamilson FreitasGuimarães, Frederico Gadelha2018-01-262018-01-262016COELHO, 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. 2018.1876-6102http://www.repositorio.ufop.br/handle/123456789/9365As 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.en-USabertoMicrogridHousehold electricity demandDeep learningGraphics processingA hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.Artigo publicado em periodicoThis 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.https://doi.org/10.1016/j.egypro.2016.11.286