Transfer learning for short-term wind speed prediction with deep neural networks

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical parad...

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Veröffentlicht in:	Renewable energy Jg. 85; S. 83 - 95
Hauptverfasser:	Hu, Qinghua, Zhang, Rujia, Zhou, Yucan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2016
Schlagworte:	Deep neural networks learning neural networks prediction Stacked denoising autoencoder Transfer learning uncertainty weather forecasting wind farms wind power wind speed Wind speed prediction Wind speed prediction Deep neural networks Transfer learning Stacked denoising autoencoder
ISSN:	0960-1481, 1879-0682
Online-Zugang:	Volltext
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