Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm

Abstract Wind power is one of the most promising powers. Wind speed forecasting can eliminate the harmful effect caused by the intermittent and fluctuation of wind power, and big multi-step forecasting can provide more time for the power grid to be adjusted. To achieve the high-precision big multi-step forecasting, a novel hybrid model named as the WD-SampEn-VMD-MadaBoost-BFGS-WF is proposed in the study, which consisting of three main modeling steps including the secondary decomposition, the ensemble method and the error correction. The detail of the proposed model is given as follows: (a) wind speed series are decomposed by the WD (Wavelet Decomposition) to obtain wind speed subseries. The SampEn (Sample Entropy) algorithm is used to estimate the unpredictability of these wind speed subseries. The most unpredictable subseries will be decomposed secondarily by the VMD (Variational Mode Decomposition); (b) the subseries are proceeded by the MAdaBoost (Modified AdaBoost.RT) with the BFGS (Broyden–Fletcher–Goldfarb–Shanno Quasi-Newton Back Propagation) neuron network to obtain forecasting subseries; (c) all of the forecasting subseries will be combined with the original subseries to form the combined wind speed series, which will be further proceeded by the WF (Wavelet Filter) to obtain the corrected forecasting series from the point of the frequency domain; (d) the corrected forecasting series are reconstructed to get the final forecasting series. To validate the effectiveness of the proposed model, several forecasting cases are provided in the study. The result indicates that the proposed model has satisfactory forecasting performance in the big multi-step extremely strong simulating wind speed forecasting.

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