基于掩膜经验模态分解和ELM 的风速预测

针对随空间、时间呈现非平稳、非线性变化的特征,提出基于极限学习机和掩膜经验模态分解的组合短期风速预测方法.首先,风速序列的非平稳性特征对风速预测结果有较大影响,利用掩膜经验模态分解的方法将风速序列分解成对平稳的不同频率的分量,解决其存在的非平稳性问题;其次,为处理极限学习机的输入维数随意性选取问题,对风速序列分解不同频率的分量进行相空间重构;最后,利用ELM神经网络方法对各分量建立预测模型.实验结果表明:该预测方法在短期风速序列预测中取得了理想的预测效果,提高了算法精度,具有先进性和有效性.

Short-term Wind Speed Forecasting by Combination of Masking Signal-based Empirical Mode Decomposition and Extreme Learning Machine

In view of the wind speed series which changes with the time and space and shows the non-linear and non-stationary characteristics, this paper proposes a short-term combination prediction model of the wind speed by means of the extreme learning machine (ELM) and masking signal-based empirical mode decomposition (MS-EMD). Firstly, because of the non-stationary characteristics of the wind speed series, the wind speed series is decomposed into several components with different frequency bands by the MS-EMD to reduce the non-stationary characteristics. Secondly, in order to avoid the randomness of input dimensionality selection of the ELM, the phase space of each component is reconstructed. Thirdly, the ELM model of each component is established to predict the wind speed series, and the predicted results of each component are added to get the final result. The simulation result verifies that the method is able to acquire great forecasting results in short-term wind speed series forecast, and further improves the method accuracy. It is advanced and effectively.