An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network

Abstract

To raise the wind speed prediction accuracy, Wavelet Transform (WT) is widely employed to disaggregate an original wind speed series into several sub series before forecasting. However, the highest frequency sub series usually has a great disturbance on the final prediction. In the study, for raising the forecasting accuracy, Singular Spectrum Analysis (SSA) is applied to make further processing on the highest frequency sub series, instead of making no modification on or getting rid of it. So a hybrid decomposition technology called Improved WT (IWT) is proposed. Meanwhile, a new hybrid model IWT-ENN combined with IWT and Elman Neural Network (ENN) is also designed. The procedure of IWT is systematically investigated. Experimental results show that: (1) the performance of the hybrid model IWT-ENN has a great improvement compared to that of others including the persistence method, ENN, Auto-Regressive (AR) model, Back Propagation Neural Network (BPNN) and Empirical Mode decomposition (EMD)-ENN; (2) compared to the two general strategies where the highest frequency sub series is without retreatment or eliminated, the new proposed hybrid model IWT-ENN has the best prediction performance.