基于AVMD-CNN-GRU-Attention的超短期风功率预测研究

RESEARCH ON ULTRA-SHORT-TERM WIND POWERFORECAST BASED ON AVMD-CNN-GRU-Attention

为提高超短期风功率的预测精度,提出一种改进的基于变分模态分解的卷积神经网络(AVMD-CNN)、门控循环单元(GRU)和注意力机制(Attention)的超短期风功率预测模型。首先利用改进的VMD将风功率序列分解为K个子模态;然后将各子模态利用样本熵(SE)和中心频率进行分类,根据分类结果对各子模态分别给定归一化方式,并按SE值分别输入到GRU-Attention和CNN-GRU-Attention模型中进行训练和预测;最后将各子模态预测结果叠加得到最终结果,从而完成超短期风功率预测。以决定系数(R^(2))、平均绝对误差(MAE)、均方根误差(RMSE)以及平均绝对百分比误差(MAPE)为精度评估指标,实际算例表明,所提出模型的R^(2)较文中其他方法平均提高12.06%,MAE、RMSE以及MAPE分别平均降低59.36%、62.49%和48.34%,具有较高的预测精度。

In order to improve the forecast accuracy of ultra-short-term wind power,an improved ultra-short-term wind power forecast model based on variational mode decomposition convolutional neural network(AVMD-CNN),gated recurrent unit(GRU)and attention mechanism(Attention)is proposed.Firstly,the wind power sequence is decomposed into K sub-modes by using the improved VMD.Then,each sub-mode is classified by sample entropy(SE)and center frequency.According to the classification results,each sub-mode is given a normalization method,and input into GRU-Attention and CNN-GRU-Attention models for training and forecasting according to SE values.Finally,the final results are obtained by superimposing the forecast results of each sub-mode,so as to complete the ultrashort-term wind power forecast.Using the determination coefficient(R^(2)),mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)as the accuracy assessment indexes,the actual arithmetic examples show that the R^(2)of the proposed model is improved by 12.06%on average compared with other methods,and the MAE,RMSE,and MAPE are reduced by 59.36%,62.49%,and 48.34%respectively,with high prediction accuracy.