Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks

Abstract

The wind speed forecasting is an important technology for the management of the wind energy. In this study, a new hybrid framework using the WPD (Wavelet Packet Decomposition), the CEEMDAN (Complete Ensemble Empirical Mode Decomposition) and the ANN (Artificial Neural Network) is proposed for wind speed multi-step forecasting. In the proposed framework, the WPD is employed to decompose the original wind speed series into a series of sub-layers, while the CEEMDAN is adopted to further decompose all the obtained sub-layers into a number of IMFs (Intrinsic Mode Functions). Finally, three types of ANN models, including the BP (Back-propagation Neural Network) models, the RBF (Radial Basis Function Neural Network) models and the GRNN (General Regression Neural Network) models, are utilized to complete the predicting computation for the decomposed wind speed series, respectively. To investigate the prediction performance of the presented framework, nine models are included in the comparisons as: the BP model, the WPD-BP model, the WPD-CEEMDAN-BP model, the RBF model, the WPD-RBF model, the WPD-CEEMDAN-RBF model, the GRNN model, the WPD-GRNN model and the WPD-CEEMDAN-GRNN model. Two experimental results indicate that: the proposed WPD-CEEMDAN-ANN models have better performance than the involved corresponding ANN models and WPD-ANN models in three-step predictions.