基于改进经验小波变换和最小二乘支持向量机的短期风速预测

SHORT-TERM WIND SPEED FORECASTING BASED ON IMPROVED EMPIRICALWAVELET TRANSFORM AND LEAST SQUARES SUPPORT VECTOR MACHINES

针对原始风速信号非线性和非平稳性的特征,提出一种新的改进经验小波变换(IEWT)方法,该方法可将风速信号分解成一组有限带宽的子序列,以降低其不稳定性。在此基础上,结合最小二乘支持向量机(LSSVM),提出基于改进经验小波变换和最小二乘支持向量机(IEWT-LSSVM)的短期风速预测方法,并通过模拟退火粒子群优化算法(SAPSO)对相空间重构参数以及LSSVM模型的2个超参数进行共同优化。最后以华北某风电场采集的风速信号为算例,结果表明基于IEWT-LSSVM的预测模型能有效追踪风速信号的变化,在单步预测和多步预测上均具有较高的预测精度。

A new improved empirical wavelet transform(IEWT)method is proposed to treat with the nonlinearity and nonstationarity of original wind speed signal.This method decomposes wind speed signal into a set of band-limited sub-sequences to decrease instability.On this basis,combined with least squares support vector machine(LSSVM),a short-term wind speed forecasting model based on IEWT-LSSVM is proposed.The phase space reconstruction parameters and the hyper parameters of LSSVM model are optimized by simulated annealing particle swarm optimization(SAPSO).Finally,taking the wind speed data of a certain wind farm in North China as an example,the simulation results illustrate that the forecasting model based on IEWT-LSSVM can effectively track the change of wind speed signal,has high prediction accuracy in single-step prediction and multi-step prediction.