基于自适应二次分解与CNN-BiLSTM的超短期风电功率预测

ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ONADAPTIVE QUADRATIC MODE DECOMPOSITION AND CNN-BiLSTM

为提高风电功率预测精度,提出基于自适应二次模态分解(QMD)、卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)的超短期风电功率预测模型。针对风电功率的波动性,利用改进的完全自适应噪声集成经验模态分解方法(ICEEMDAN)对风电功率数据进行分解。引入麻雀搜索算法(SSA)对变分模态分解(VMD)的分解数量与惩罚因子进行优化,使VMD具有自适应性。将ICEEMDAN分解得到的高频分量I_(1)用SSA-VMD进行第二次分解,降低序列不平稳度。同时,构建包含2层池化层的CNN网络进行特征提取与BiLSTM网络的超短期预测模型,最终的风电功率即为各子序列预测结果之和。通过算例分析进行实验表明,所提风电功率预测方法的预测精度优于其他模型,验证了预测模型的优越性。

For the purpose of promotion the precision of wind power forecasting,an ultra-short-term wind power forecasting model based on adaptive quadratic mode decomposition,convolutional neural networks and bidirectional long-short term memory network is proposed.In view of the fluctuation of wind power,using the improved fully adaptive noise integrated empirical mode decomposition method to decompose the wind power data.The sparrow search algorithm is introduced to optimize the decomposition number and penalty factor of variational mode decomposition,so that VMD has adaptability.The high-frequency component I_(1) obtained by decomposition of ICEEMDAN is decomposed secondarily by SSA-VMD to reduce the sequence instability.At the same time,the CNN network containing two pooling layers is constructed for feature extraction with the ultra-short-term prediction model of BiLSTM network,and the prediction results of each subsequence are superimposed to obtain the final wind power output prediction results.Experiments conducted through the analysis of arithmetic examples show that the prediction accuracy of the proposed wind power prediction method is better than other models,which verifies the superiority of the prediction model.