A hybrid algorithm combining auto-encoder network with Sparse Bayesian Regression optimized by Artificial Bee Colony for short-term Wind Power Forecasting

• Autorzy: Li Y. , Yang R.

Warianty tytułu

PL

Krótkoterminowe przewidywanie energii wiatru przez algorytm hybrydowy – sieć auto-enkoderowa oraz regresja Bayesa SBR zoptymalizowana metodą sztucznej kolonii pszczół

• Języki publikacji: EN

Abstrakty

EN

To forecast the short-term wind power precisely, this paper proposes a hybrid strategy which consists of a nonlinear dimensionality reduction component by auto-encoder network and a forecasting component based on Sparse Bayesian Regression optimized by Artificial Bee Colony Optimization. The proposed model can predict wind power curve per hour with a lead time of 3hours. Finally, an experiment is conducted to test the effectiveness of the forecasting model based on the detailed data from a wind farm in China.

PL

W artykule zaproponowano hybrydową metodę przewidywania krzywej prędkości wiatru w okresie kolejnej godziny. Algorytm bazuje na nieliniowej redukcji wymiarowości przez sieć auto-enkoderową (sztuczną sieć neuronową) oraz na elemencie przewidującym, opartym na rzadkiej regresji Bayesa (ang. Sparse bayesian Regression) zoptymalizowanej metodą sztucznej koloni pszczół.

Słowa kluczowe

EN

short-term wind power prediction; auto-encoder network; Sparse Bayesian Regression; Artificial Bee Colony

PL

predykcja wiatru krótkoterminowa; sieć auto-enkoderowa; SBR; sztuczna kolonia pszczół

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 2a
• Strony: 223--228
• Opis fizyczny: Bibliogr. 26 poz., schem., tab., wykr.

Twórcy

autor

Li Y.

• North China Electric Power University, Beijing,102206,China

autor

Yang R.

• North China Electric Power University,Beijing,102206,China

Bibliografia

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