A GPU deep learning metaheuristic based model for time series forecasting. (1st September 2017)

• Record Type: Journal Article
• Authors: Coelho, Igor M.; Coelho, Vitor N.; Luz, Eduardo J. da S.; Ochi, Luiz S.; Guimarães, Frederico G.; Rios, Eyder
• Is Part Of: Applied energy. Volume 201(2017)
• Page Start: 412
• Page End: 418
• Publication Date: 2017-09-01
• Subjects: Deep learning -- Graphics processing unit -- Hybrid forecasting model -- Smart sensors -- Household electricity demand -- Big data time-series; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.01.003
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A CPU-GPU mechanism is proposed in order to accelerate time series learning. Disaggregated household energy demand forecasting is used as case of study. Suggestions to embed the proposed low energy GPU based system into smart sensors. Parallel forecasting model accuracy evaluation with a metaheuristic training phase. Abstract: 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.