A Hybrid Deep Learning Forecasting Model Using GPU Disaggregated Function Evaluations Applied for Household Electricity Demand Forecasting

• Published in: Energy procedia Vol. 103; pp. 280 - 285
• Main Authors: Coelho, Vitor N., Coelho, Igor M., Rios, Eyder, Filho, Alexandre S.T., Reis, Agnaldo J.R., Coelho, Bruno N., Alves, Alysson, Netto, Guilherme G., Souza, Marcone J.F., Guimarães, Frederico G.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2016
• Subjects: Big Time-series Data; Deep Learning; Graphics Processing Unit; Household Electricity Demand; Microgrid; Parallel forecasting model; Graphics Processing Unit; Microgrid; Parallel forecasting model; Big Time-series Data; Deep Learning; Household Electricity Demand
• ISSN: 1876-6102; 1876-6102
• DOI: 10.1016/j.egypro.2016.11.286

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.