Tatinati Sivanagaraja, Veluvolu Kalyana C
School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea.
ScientificWorldJournal. 2013 Dec 24;2013:548370. doi: 10.1155/2013/548370. eCollection 2013.
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.
我们提出了一种用于预测风速的混合方法。首先利用经验模态分解将风速数据分解为固有模态函数（IMF）。然后基于各个IMF的偏自相关因子，采用自适应方法对IMF进行预测。对于相关因子较弱的IMF，采用最小二乘支持向量机；对于相关因子较高的IMF，采用带卡尔曼滤波器的自回归模型。使用所提出的混合方法进行多步预测可提高预测效果。风速数据的结果表明，与现有方法相比，所提出的方法能提供更好的预测。
