Wind speed prediction based on CEEMD-SE and multiple echo state network with Gauss-Markov fusion

The purpose of this study is to improve the prediction accuracy of wind speed. The wind speed has the characteristics of unstable, non-stationary, and non-linear, so it is difficult to predict the wind speed. This study proposes a prediction model based on the complementary ensemble empirical mode decomposition-sample entropy and multiple echo state network (ESN) with Gauss-Markov fusion for wind speed. The proposed prediction model consists of the following steps: (a) using the complementary ensemble empirical mode decomposition algorithm, it decomposes the initial wind speed time series and obtains some components with different scales, and (b) using the sample entropy algorithm, it determines the complexity of each component. The components whose entropy is larger than the original wind speed remain unchanged, while the components whose entropy is smaller than the original wind speed are merged into one. The reconstructed component greatly reduces the number of prediction models. (c) After reconstruction, the ESN has good regression prediction ability, so it is chosen as the prediction model of each component. The gray wolf optimization algorithm is introduced to optimize the parameters of the ESN. (d) The Gauss-Markov algorithm is adopted to fuse the predicted values of multiple ESN models. The variance of the predicted value obtained using the Gauss-Markov fusion is less than that of the single ESN model, which significantly increases the prediction accuracy. In order to verify the prediction performance of the proposed model, the actual ultra-short-term and short-term wind speed sample data are compared. At the same time, seven prediction models are chosen as the comparison model. Finally, through the comparison of the prediction error and its histogram distribution, eight performance indicators, Pearson's correlation coefficient, and Diebold-Mariano test, all the results show that the proposed prediction model has high prediction accuracy.