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

Yong Qin, Kun Li, Zhanhao Liang, Brendan Lee, Fuyong Zhang, Yongcheng Gu, Lei Zhang, Fengzhi Wu and Dragan Rodriguez

Applied Energy, 2019, vol. 236, issue C, 262-272

Abstract: 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.

Keywords: Wind signal; Forecasting; Long short term memory network; Multi task learning, deep neural networks (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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DOI: 10.1016/j.apenergy.2018.11.063

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