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

Author

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

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.

Suggested Citation

  • Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:262-272
    DOI: 10.1016/j.apenergy.2018.11.063
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    23. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    24. Xiao Wang & Zheng Zheng & Guoqian Jiang & Qun He & Ping Xie, 2022. "Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network," Energies, MDPI, vol. 15(8), pages 1-19, April.
    25. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).

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