基于IEEMD与LS-SVM组合的短期风电功率多步预测方法

针对组合预测方法中经验模态分解(EMD)部分存在处理非线性和非稳态信号的不足,提出了一种改进的集总经验模态分解(IEEMD)与最小二乘支持向量机(LS-SVM)模型相结合的短时风电功率预测方法。该方法首先通过对加噪辅助分解方法噪声准则的研究,推导出加噪方式采用正负成对形式可以有效消除分量中的残余噪声,且确定加噪幅值和分解次数采取固定值:0. 014 SD和2次。然后将原始数据通过IEEMD方法分解成一系列固有模态函数,运用游程判定法进行筛选重构成高中低频三种频段,并对不同频段的分量建立LS-SVM多步预测模型,最后将预测值自适应叠加作为最终的预测结果。通过仿真实验和实测风电功率实验验证了所提方法在预测精度上具有一定优势,为短时预测方法提供了一种新思路。

Short-Term Wind Power Multi-Step Prediction Method Based on the IEEMD and LS-SVM

Aiming at the shortcomings that the Empirical Mode Decomposition (EMD) part in the combination forecasting method is not insufficient in processing non-linear and non-stationary signal, a short-term wind power prediction method based on hybrid Improved Ensemble Empirical Mode Decomposition (IEEMD) and Least Squares-Support Vector Machine (LS-SVM) model was proposed. Firstly, through the study of the added-noise principle of noise assisted decomposition method, the additive noises applied in the form of positive and negative pairs were deduced to effectively eliminate the residual noise within the components, and the two additive noises parameters of the amplitude of additive white-noise and the number of ensemble trials were determined to fixed as 0.014 times standard deviation of the original signal and two ensemble trials ,respectively .Furthermore, the original data was decomposed into a series of Intrinsic Mode Functions (IMFs) by IEEMD method, which screened and restructured into three frequency range components with high frequency, intermediate frequency and low frequency by the run-lengths test. And then, those components with different frequency bands were established for the LS-SVM multi-step prediction models. Finally the prediction values were adaptively superposed to obtain the predicted result. Through the simulation experiments and the measured wind power experiments verify that the proposed method has certain advantages in prediction accuracy, which also provides a novel idea for the short-term prediction method.