A hybrid approach for short-term forecasting of wind speed.
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.
School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea.
Sivanagaraja
S
Tatinati
School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea.
Kalyana C
KC
Veluvolu
D000465:Algorithms; D005544:Forecasting / Q000379:methods; D016018:Least-Squares Analysis; D015233:Models, Statistical; D008962:Models, Theoretical; D012044:Regression Analysis; D060388:Support Vector Machine; D014919:Wind