基于多模式分解和麻雀优化残差网络的短期风速预测模型

SSA-Res-GRU Short-term Wind Speed Prediction Model Based on Multi-model Decomposition

为提高风速预测的精度,提出一种基于多模式分解、麻雀优化算法(sparrow search algorithm,SSA)、残差网络(residual neural network,ResNet)和门控循环单元网络(gated recurrent units,GRU)的短期风速预测模型。该模型首先利用小波分解(wavelet transform,WT)、变分模态分解(variational mode decomposition,VMD)和自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)分别分解经过模糊C均值聚类后的风速数据,不同模态分解分量组合为二维矩阵,作为卷积网络的输入数据,实现不同模式分量波动规律的互补;随后,在传统卷积网络结构中增加改进的残差模块,对多模式分解分量进行特征提取,使得深层特征得到显著增强;最后,将特征融合后输入GRU模块,进一步挖掘风速分量在时序上的特征,通过麻雀优化对Res-GRU中的关键参数进行寻优,实现风速预测。实验表明,与传统组合模型相比,所提组合预测模型可以有效提高风速短期预测的准确率。

This paper proposes a combination forecasting method based on the multi-mode decomposition.the sparrow search algorithm(SSA),the residual neural network(Res Net)and the gated recurrent units(GRU)network to improve the accuracy of wind speed prediction.Firstly,the wind speed time series clustered by the fuzzy C-mean method is decomposed into three modes of multi-scale subsequences by the wavelet transform,the variational mode decomposition and the complete ensemble empirical mode decomposition with adaptive noise.The wind speed subsequences obtained by the three decomposition methods are combined into a matrix,which is input to the convolutional network.Then the multi-scale subsequences of the three different modes achieve the complementarity of the fluctuation patterns.Subsequently,the improved residual module is added into the traditional convolution network for the feature extraction of the multimodal decomposition components,which enhances the deep features significantly.Finally,in order to further extract the features of wind speed components in time series,the features are combined and input into the GRU module.Besides,the key parameters are optimized in the Res-GRU with the SSA.By applying these steps,the wind speed prediction is achieved.Experimental shows that the combined prediction model proposed in this paper effectively improves the accuracy of wind speed prediction compared with the traditional model.