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

Citation

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, Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal, Applied Energy, 236 pp. 262-272. ISSN 0306-2619 (2019) [Refereed Article]

Copyright Statement

Copyright 2018 Elsevier Ltd.

DOI: doi:10.1016/j.apenergy.2018.11.063

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.

Item Details

Item Type:Refereed Article
Keywords:wind signal, forecasting, long short term memory network, multi task learning, deep neural networks
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Wind energy
UTAS Author:Lee, B (Mr Brendan Lee)
ID Code:135048
Year Published:2019
Web of Science® Times Cited:43
Deposited By:Engineering
Deposited On:2019-09-24
Last Modified:2020-01-14
Downloads:0

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