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Neural-network-based ultra-short-term wind forecasting (CROSBI ID 609670)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Đalto, Mladen ; Vašak, Mario ; Baotić, Mato ; Matuško, Jadranko ; Horvath, Kristian Neural-network-based ultra-short-term wind forecasting // Proceedings of the European Wind Energy Association 2014 Annual Event (EWEA 2014). 2014. str. 1-8

Podaci o odgovornosti

Đalto, Mladen ; Vašak, Mario ; Baotić, Mato ; Matuško, Jadranko ; Horvath, Kristian

engleski

Neural-network-based ultra-short-term wind forecasting

In recent years rapid growth of wind power generation in many countries around the world has highlighted the importance of wind prediction. In this work neural networks are used for ultra- short-term wind prediction. In many instances reported in the literature neural network exhibit poor performance - very often because no complexity reduction methods were considered. To that end, in this paper two input variable selection algorithms based on partial mutual information are compared for further use with nonlinear models such as neural networks. Performance improvements of the proposed prediction system are compared to neural networks without input variable selection, and validated for locations near Split, Croatia. The use of neural network drastically outperforms simple persistence estimator on 3 hour horizon.

neural networks ; ultra short term ; partial mutual information

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Podaci o prilogu

1-8.

2014.

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the European Wind Energy Association 2014 Annual Event (EWEA 2014)

Podaci o skupu

European Wind Energy Association 2014 Annual Event (EWEA 2014)

poster

10.03.2014-13.03.2014

Barcelona, Španjolska

Povezanost rada

Temeljne tehničke znanosti