基于EEMD和ARCH的风电功率超短期预测

针对风电功率具有非平稳性和波动集群现象,提出一种基于集合经验模态分解和自回归条件异方差组合模型预测方法。该方法通过EEMD分解法将风电出力分解为一系列平稳的时序分量,再由游程判定法,将时序分量重组为波动分量、短期趋势分量和长期趋势分量,以集中分量特征信息降低预测难度；针对各分量的波动特征,建立相应的ARCH预测模型。算例结果表明,该种组合预测方法简单,具有较高的预测精度,能更好的反应风电功率的波动特性。

Ultra-Short-Term Wind Power Forecasting Based on EEMD and ARCH

The wind powerhas the qualifications of random and volatility concentration.This paper present a combined prediction based on ensemble empirical mode decomposition (EEMD) and autoregressive conditional heteroskedasticity model(ARCH).By means of the EEMD,the wind power sequence is decomposed into a series of stationary component.Then the components are reconstructed into fluctuant components,medium-term trend and long-term trend components for centralizing the characteristic information and reducing the difficulty of predicting.Finally,considering different types of components,the different ARCH model is built.Simulation results show that the combined prediction can offer more accurate forecasting result and reflect the fluctuation of wind power.