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Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal

Qin, Y, Li, K, Liang, Z, Lee, B, Zhang, F, Gu, Y, Zhang, L, Wu, F and Rodriguez, D 2019 , 'Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal' , Applied Energy, vol. 236 , pp. 262-272 , doi: 10.1016/j.apenergy.2018.11.063.

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This paper proposed a training-based method for wind turbine signal forecasting. This proposed model employs a convolutional network, a long short-term memory network as well as a multi-task learning ideas within a signal frame. This method utilized the convolutional network for exploitation of spatial properties from wind field. As well, the mentioned long short-term memory is used for training dynamic features of the wind field. The ideas stated together have been utilized for modeling the impacts of spatio-dynamic construction of wind field on wind turbine responses of interest. So, we implemented this multi-task training method for forecasting the generated WT energy and demand at the same time through a single forecast method, which is the deep neural-network. Performance of our suggested model is confirmed by a real wind field information that is produced by Large Eddy Simulation. This data also include wind turbine reaction information that is simulated using aero-elastic wind turbine construction analyzing software. The obtained results depict that the suggested method can forecast two outputs with a five-percent error by a so short term prediction, which is shorter than 1 m.

Item Type: Article
Authors/Creators:Qin, Y and Li, K and Liang, Z and Lee, B and Zhang, F and Gu, Y and Zhang, L and Wu, F and Rodriguez, D
Keywords: wind signal, forecasting, long short term memory network, multi task learning, deep neural networks
Journal or Publication Title: Applied Energy
Publisher: Elsevier
ISSN: 0306-2619
DOI / ID Number: 10.1016/j.apenergy.2018.11.063
Copyright Information:

Copyright 2018 Elsevier Ltd.

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