论文标题
用于短期风速预测的小波，AR和SVM的混合方法
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction
论文作者
论文摘要
风速建模和预测已经变得重要，因为它在风能管理的各个阶段都具有重要作用。在本文中，我们提出了一个基于小波变换的混合模型，以提高短期预测的准确性。使用小波分解技术将风速速度时间序列分为各种频率组件，每个频率组件分别进行建模。由于与高频范围相关的组件显示随机性质，因此我们使用自回归（AR）方法和其余的低频组件对它们进行了建模，该组件以支持向量机（SVM）建模。与独立AR或SVM模型相比，混合方法的结果显示了风速预测准确性的有望提高。
Wind speed modelling and prediction has been gaining importance because of its significant roles in various stages of wind energy management. In this paper, we propose a hybrid model, based on wavelet transform to improve the accuracy of the short-term forecast. The wind speed time series are split into various frequency components using wavelet decomposition technique, and each frequency components are modelled separately. Since the components associated with the high- frequency range shows stochastic nature, we modelled them with autoregressive (AR) method and rest of low-frequency components modelled with support vector machine (SVM). The results of the hybrid method show a promising improvement in accuracy of wind speed prediction compared to that of stand-alone AR or SVM model.
