基于EWT-KELM方法的短期风电功率组合预测

针对短期风电功率预测,提出一种基于经验小波变换(Empirical Wavelet Transform,EWT)预处理的核极限学习机(Extreme Learning Machine With Kernels,KELM)组合预测方法。首先采用EWT对风电场实测风速数据进行自适应分解并提取具有傅立叶紧支撑的模态信号分量,针对每个分量分别构建KELM预测模型,最后对各个预测模型的输出进行叠加得到风速预测值并根据风电场风功特性曲线可得对应风电功率预测值,为验证本文方法的有效性,将其应用于国内某风电场的短期风电功率预测中,在同等条件下,与KELM方法、极限学习机(Extreme Learning Machine,ELM)方法、支持向量机(Support Vector Mmachine,SVM)方法以及BP (Back Propagation Neural Network)方法对比,实验结果表明,本文所提方法具有较好的预测精度和应用潜力。

Wind power short-term forecasting based on empirical wavelet transform and extreme learning machine with kernels method

Aiming at short-term wind power focasting, a kind of combining forecasting method for short-term wind power based on empirical wavelet transform (EWT) and extreme learning machine with kernels (KELM) is proposed. firstly the EWT method is used to decompose the wind speed data and extract the different modes which have a compact support Fourier spectrum. Secondly, different KELM forecasting models are constructed for the sub-sequences formed by the each mode component. Simultaneously, the ultimate wind speed forecasting results can be obtained by the superposition of the corresponding forecasting model, the forecast value of wind power is calculated by the wind power characteristic curve. In order to verify the effectiveness of the proposed methods,it is applied to some wind farms in China for short-term wind power forcasting. The experiments are also implemented in the ELM method, KELM method, SVM method and BP methodin the same condition respectively. The comparing experimental results show that proposed method have higher forecasting accuracy and superior application potential.