Accurate wind speed forecasting is beneficial to the management of the wind power system. A hybrid WPD-DA-NAR wind speed forecasting model under moving window framework is proposed in this study. The WPD (Wavelet Packet Decomposition) is utilized to process the original wind speed time series. Since the mother wavelet function is the core component of the WPD, the matching relationship between the predictor and 17 different mother wavelet functions is discussed. The optimal mother wavelet for the application of the proposed hybrid model is determined. The NAR (Nonlinear Autoregressive) network is employed to build the forecasting models for decomposed sub-layers. A hybrid optimization framework base on the DA (Dragonfly algorithm) is adopted to optimize the NAR network. In order to capture the characters of wind speed time series and update model accordingly, the optimized NAR is applied under a moving window framework. The proposed hybrid model is compared with 8 existing models. The experimental results indicated that: (a) the dmey wavelet provides the best results among all the included 17 mother wavelet functions; (b) the proposed hybrid WPD-DA-NAR model under moving window framework has the best performance in all steps and metrics, compared with 8 existing models.

准确的风速预测有利于风电系统的管理。提出了一种在移动窗口框架下的WPD-DA-NAR混合风速预测模型。WPD（小波包分解）用于处理原始风速时间序列。由于母小波函数是WPD的核心组成部分，因此讨论了预测子与17种不同母小波函数之间的匹配关系。确定了用于所提出的混合模型的最佳母小波。NAR（非线性自回归）网络用于构建分解子层的预测模型。基于DA（Dragonfly算法）的混合优化框架。）来优化NAR网络。为了捕获风速时间序列的特征并相应地更新模型，在移动窗口框架下应用了优化的NAR。将提出的混合模型与8个现有模型进行比较。实验结果表明：（a）dmey小波在包括的所有17种母小波函数中提供了最好的结果；（b）与现有的8种模型相比，在移动窗口框架下提出的WPD-DA-NAR混合模型在所有步骤和指标上均具有最佳性能。