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

journal contribution
posted on 2023-05-20, 07:17 authored by Qin, Y, Li, K, Liang, Z, Lee, B, Zhang, F, Gu, Y, Zhang, L, Wu, F, Rodriguez, D
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.

History

Publication title

Applied Energy

Volume

236

Pagination

262-272

ISSN

0306-2619

Department/School

School of Engineering

Publisher

Elsevier

Place of publication

Oxford, England

Rights statement

Copyright 2018 Elsevier Ltd.

Repository Status

  • Restricted

Socio-economic Objectives

Wind energy

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