分别运用经验模态分解(empirical mode decomposition, EMD)法和快速集合经验模态分解(fast ensemble empirical mode decomposition, FEEMD)法将非平稳下击暴流风速分解为一系列稳态序列集,即固有模态分量. 建立极限学习机(extreme learning machines, ELM)风速预测模型(EMD-ELM)和快速EMD-ELM(FEEMD-ELM),分别对分解后的非平稳脉动风速训练集和测试集进行预测. 同时,将EMD和FEEMD与基于粒子群优化(particle swarm optimization, PSO)最小二乘支持向量机(least squares support vector machine, LSSVM)进行混合, 形成EMD-PSO-LSSVM和FEEMD-PSO-LSSVM混合模型算法. 通过比较这4种预测算法的结果发现,基于EMD-ELM和FEEMD-ELM的非平稳下击暴流风速预测模型更为准确高效,其中FEEMD-ELM模型预测最佳.

Using the empirical mode decomposition (EMD) and fast ensemble empirical mode decomposition (FEEMD), respectively, a nonstationary downburst wind velocity sample can be decomposed into a series of stationary sequence sets, namely, intrinsic mode functions. Extreme learning machines based on EMD and FEEMD, referred to as the EMD-ELM and FEEMD-ELM, are proposed to forecast the training and testing sets, both partitioned to stationary sequence sets. Meanwhile, combining EMD and FEEMD with a least squares support vector machine (SVM) based on particle swarm optimization, EMD-PSO-LSSVM and FEEMD-PSO-LSSVM algorithms are obtained. Comparison of these four prediction algorithms shows that EMD-ELM and FEEMD-ELM are more accurate and efficient in predicting nonstationary downburst wind velocity, while FEEMD-ELM is the best.