A Combined Model Based on CEEMDAN, Permutation Entropy, Gated Recurrent Unit Network, and an Improved Bat Algorithm for Wind Speed Forecasting

Accurate and reliable wind speed forecasting is crucial for wind farm planning and grid operation security. To improve the accuracy of wind speed forecasting, a novel combined model is proposed for wind speed forecasting in this article. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and permutation entropy (PE) are employed to decompose the original wind speed time series into the sub-series with obvious complexity different; To overcome the disadvantage of weak generalization ability of single deep learning method when facing diversiform data, a cluster of gated recurrent unit networks (GRUs) with different hidden layers and neurons are applied to capturing the unsteady characteristics and implicit information of each sub-series; The predictions of the GRUs of each sub-series are aggregated into a nonlinear-learning regression top-layer which is consisted of radial basis function neural network (RBFNN), and improved bat algorithm (IBA) is introduced to optimize the parameters of RBFNN; Lastly, the prediction values of each nonlinear-learning top-layer are superimposed to obtain the final prediction values. To validate the effectiveness of the model, 15-min and 1-h wind speed data from the wind farm in Zhangjiakou, China, are used as test cases. The experimental results demonstrate that the proposed combined model can achieve the best performance and stability compared to other models. Such as the performance evaluation indexes (RMSE = 0.3294, MAPE = 2.6169%) are smallest obtained from case study 1, and (RMSE = 0.5876, MAPE = 4.7875%) are smallest obtained from case study 2.