基于麻雀算法优化的VMD⁃CNN⁃LSTM的短期风电功率研究

Research on Short-term Wind Power Forecasting Based on VMD-CNN-LSTM Optimized by Sparrow Algorithm

考虑相关风电场之间的影响因素可以有效提升新建风电场的风电功率预测精度,提出利用变分模态分解技术(VMD)将单风电场风电功率预处理分解为本征模态函数(IMF),然后将各风电场同频段分量,即低频分量、高频分量和残差分量,组合为二维特征矩阵作为卷积神经网络(CNN)的输入,利用卷积神经网络提取同分量子模态下空间特征信息,输入到长短时记忆网络(LSTM)提取时间序列中的长时依赖关系进行预测,最后将预测结果进行叠加,获得完整的预测结果。组合神经网络的超参数设置相较于单一模型对预测精度的影响更大,采用新型麻雀搜索算法(SSA)可以节省人工手动调制参数的时间、提高超参数设置的精度和效率。使用该方法对某风电集群中的新建基准风电场进行预测,预测结果表明经SSA优化的VMD-CNN-LSTM模型在预测风电集群数据上有较高的精度,预测效果好于对比模型LSTM,CNN-LSTM和SSA-VMD-LSTM。

In order to improve the prediction accuracy of wind power in new wind farms effectively,the influencing factors between relevant wind farms were considered.A variational mode decomposition(VMD)technique was proposed to decompose the wind power preprocessing of a single wind farm into intrinsic mode function(IMF),and then the same frequency band component such as low-frequency components,high-frequency components and residual components of each wind farm were combined respectively as the input of convolution neural network(CNN).CNN was used to extract the characteristic information under the same split sub-mode,which was input to the long short-term memory(LSTM)network for prediction,and finally the prediction results were overlaid to obtain the complete prediction results.Compared with a single model,the hyperparameter setting of the combined neural network will affect the prediction accuracy more.A new sparrow search algorithm(SSA)was proposed to save the time of manual parameter adjustment and improve the accuracy and efficiency of hyperparameter setting.The proposed method was used to predict the new benchmark wind farm in a wind power cluster,the result verifies that the VMD-CNN-LSTM optimized by SSA has a higher accuracy in predicting the wind power cluster data,which is higher than the comparison model LSTM,CNN-LSTM and SSA-VMD-LSTM.