电力系统保护与控制2012，Vol.40Issue(10)：6-11,18,7.
基于经验模式分解和神经网络的短期风速组合预测
Short-term wind speed forecasting based on EMD and ANN
摘要
Abstract
Wind speed time series, due to its strong intermittency and randomness, belongs to non-stationary time series. In order to improve the forecasting accuracy, a new forecasting model based on Empirical Mode Decomposition (EMD) and ANN is presented. By means of the EMD technique, the original wind speed series are decomposed into a series of components of different frequencies with relatively stationary variation. Thus the interferences among the characteristic information embedded in the wind speed can be weakened. According to the change regulation of each intrinsic mode function, the appropriate ANN model is chosen to forecast each intrinsic mode function. For high frequency components, we can use combination of ANN model, while use one appropriate model for low frequency components, and then add up each forecasting result to get the final forecasting value. The simulation results indicate that the accuracy of the forecasting model discussed in the paper is higher than that of RBF model and SVM model.关键词
短期预测/经验模式分解/径向基神经网络/支持向量机/广义回归神经网络/组合预测Key words
short-term prediction/ empirical mode decomposition/ RBF/ SVM/ GRNN/ hybrid forecasting分类
信息技术与安全科学引用本文复制引用
王韶,杨江平,李逢兵,刘庭磊..基于经验模式分解和神经网络的短期风速组合预测[J].电力系统保护与控制,2012,40(10):6-11,18,7.基金项目
国家"111"计划项目(B08036) （B08036）
输配电装备及系统安全与新技术国家重点实验室自主研究项目(2007DA10512709212) （2007DA10512709212）
