Short-term prediction of wind power using EMD and chaotic theory

被引：110

作者：

An, Xueli ^{[1
]}

Jiang, Dongxiang ^{[1
]}

Zhao, Minghao ^{[1
]}

Liu, Chao ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Thermal Engn, State Key Lab Control & Simulat Power Syst & Gene, Beijing 100084, Peoples R China

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2012年 / 17卷 / 02期

基金：

中国博士后科学基金;

关键词：

Power prediction; Hybrid prediction model; Empirical mode decomposition; Chaotic characteristics identification; Largest Lyapunov exponent prediction method; Grey forecasting model; GENERATION;

D O I：

10.1016/j.cnsns.2011.06.003

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Due to the strong non-linear, complexity and non-stationary characteristics of wind farm power, a hybrid prediction model with empirical mode decomposition (EMD), chaotic theory, and grey theory is constructed. The EMD is used to decompose the wind farm power into several intrinsic mode function (IMF) components and one residual component. The grey forecasting model is used to predict the residual component. For the IMF components, identify their characteristics, if it is chaotic time series use largest Lyapunov exponent prediction method to predict. If not, use grey forecasting model to predict. Prediction results of residual component and all IMF components are aggregated to produce the ultimate predicted result for wind farm power. The ultimate predicted result shows that the proposed method has good prediction accuracy, can be used for short-term prediction of wind farm power. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1036 / 1042

页数：7

共 18 条

[1] Grey prediction with rolling mechanism for electricity demand forecasting of Turkey [J].