One Hour Ahead Prediction of Wind Speed Based on Data Mining

摘要：

Wind speed forecasting is very important to the utilization of wind energy in wind farm. In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and wavelet decomposition combine with least square support vector machine (LSSVM) is proposed in this paper. The wind speed time series was decomposed into several intrinsic mode functions (IMF) and the trend term. In order to reduce the nature of nonstationary, the high frequency band was decomposed and reconstructed by wavelet transform (WT). The different LSSVM models to forecast each IMF and trend term were built up. These forecasting results of each IMF and trend term were combined to obtain the final forecasting results. The simulation experiment shows the MAPE is 4.53％ about wind speed forecasting and the prediction accuracy is improved considerably.

关键词： wind speed forecasting empirical mode decomposition wavelet transform least square support vector machine

作者: Liu Dejun Li Hui Ma Zhonghua

作者单位: Faculty of Mechanical and Electronic Engineering China University of Petroleum-Beijing,102249 Beijing,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 199-203

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）