基于经验模态分解与多步预测的最小二乘支持向量机的风速预测

由于风速信号是非线性、非稳定性的动态信号,用传统预测方法难以达到满意效果。为提高预测精度,提出了基于经验模态分解与多步预测的最小二乘支持向量机相结合的方法,对风速时间序列进行建模预测,即首先对风速动态信号进行经验模式分解,将原信号分解为若干个不同特征尺度(频率)的本征模态函数,然后对不同频带的平稳IMF分量分别建立多步预测的最小二乘支持向量机模型,将各分量的预测值等权求和得到最终预测值。实例分析结果表明,与单一的最小二乘支持向量机预测方法相比,经验模态分解与多步预测的最小二乘支持向量机相结合的风速预测方法误差小,可应用于风速预测中。

Wind Speed Forecasting Based on EMD and Multi step LS SVM

Wind speed signal is a nonlinear, unstable dynamic signal. The traditional prediction methods are difficult to achieve satisfied effect. In order to improve the prediction accuracy, a method based on empirical mode decomposition and multi step least squares support vector machine is put forward to establish the model and forecast wind speed. Empirical mode decomposition is applied to decompose dynamic wind speed signal into several intrinsic mode functions with different characteristic scale (frequency). And then multi step least squares support vector machine forecasting model corresponding to each stationary IMF component is built, and the final prediction result is the sum of each prediction component. The results of example analysis show that the error of the proposed method is smaller than that of single LS SVM prediction method. So, the proposed method can be applied to wind speed forecasting.