基于改进经验模态分解和支持向量机的短期风速组合预测

为更精确地进行风速预测,提出一种利用带自适应噪声的完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)方法和蝙蝠算法(bat algorithm,BA)优化支持向量机(support vector machine,SVM)的组合短期风速预测方法。首先用CEEMDAN对原始风速时间序列进行分解,得到一系列不同频率的子序列;其次,使用BA-SVM组合模型预测对分解后的各个子序列分别进行预测;最后,将各子序列的预测结果叠加得到风速预测值。仿真结果表明,该模型提高了预测精度,减小了误差。

Short-term Wind Speed Combination Prediction Based on Improved Empirical Mode Decomposition and Support Vector Machine

In order to predict the wind speed more accurately, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bat algorithm (BA) to optimize the support vector machine, a combined model was proposed for short-term wind speed forecasting. Firstly, CEEMDAN was used to decompose the original wind speed time series into a series of subsequences with different frequencies. Secondly, the decomposed subsequences were forecasted by combined model of BA-SVM. Finally, the wind speed forecasting results was achieved by superposing each predicted subsequence. The simulation results suggest that the model improves the prediction accuracy and reduces the error.