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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Vydané v:	Renewable energy Ročník 85; s. 83 - 95
Hlavní autori:	Hu, Qinghua, Zhang, Rujia, Zhou, Yucan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.01.2016
Predmet:	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
On-line prístup:	Získať plný text
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