As the first prerequisite to carve out the increased exploration of the wind power generation and developments, accurate wind power prediction is sufficiently reliable to eliminate the dilemma caused by its intrinsic irregularity, intermittence and non-stationary. Therefore, the paper proposes the hybrid model composed of secondary decomposition, preliminary forecasting and error analysis, which can capture the fluctuation of the wind power series better, but also guarantee the forecasting stability simultaneously. More specifically, the secondary decomposition is developed to grasp the primary trend of a wind power series; Next, random forest algorithm, kmeans clustering and Long short term memory(LSTM) network are successfully employed to infer the latent characteristics of the decomposed modes as much as possible; For the sake of estimating the uncertainty associated with the preliminary results, the process based on LSTM network models the error sequences, of which the inherent information could be further mined. Then, the final predicted values are obtained by integrating the error sequences and preliminary results. Finally, the properties of the developed model are illustrated through wind power data from two wind farms. Besides, compared with the contrastive models, the proposed model presents 88.06%,96.35% improvements in terms of Mean Relative Error(MRE), Root Mean Square Error(RMSE) at most in the two cases, which demonstrates the superiority of the proposed model.

作为开展对风力发电和发展的不断探索的第一个前提，准确的风力预测足以消除因其固有的不规则性，间歇性和非平稳性而引起的困境。因此，本文提出了一种由二次分解，初步预测和误差分析组成的混合模型，可以较好地捕获风电序列的波动，同时又能保证预测的稳定性。更具体地讲，二次分解的发展是为了把握风力系列的主要趋势。其次，成功地利用随机森林算法，kmeans聚类和长期短期记忆（LSTM）网络来尽可能地推断分解模式的潜在特征。为了估计与初步结果相关的不确定性，基于LSTM网络的过程对错误序列进行了建模，可以进一步挖掘其固有信息。然后，通过对错误序列和初步结果进行积分来获得最终预测值。最后，通过来自两个风电场的风力数据说明了所开发模型的属性。此外，与对比模型相比，该模型在两种情况下的平均相对误差（MRE），均方根误差（RMSE）最多提高了88.06％，96.35％。 。通过对误差序列和初步结果进行积分，可以获得最终的预测值。最后，通过来自两个风电场的风力数据说明了所开发模型的属性。此外，与对比模型相比，该模型在两种情况下的平均相对误差（MRE），均方根误差（RMSE）最多提高了88.06％，96.35％。 。通过对误差序列和初步结果进行积分，可以获得最终的预测值。最后，通过来自两个风电场的风力数据说明了所开发模型的属性。此外，与对比模型相比，该模型在两种情况下的平均相对误差（MRE），均方根误差（RMSE）最多提高了88.06％，96.35％。 。