Wind power short-term forecasting based on empirical mode decomposition and chaotic phase space reconstruction

被引：0

作者：

Zhang, Yiyang ^{[1
]}

Lu, Jiping ^{[1
]}

Meng, Yangyang ^{[1
]}

Yan, Huan ^{[1
]}

Li, Hui ^{[1
]}

机构：

[1] State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2012年 / 36卷 / 05期

关键词：

Empirical mode decomposition (EMD); Least squares support vector machine; Phase space reconstruction; Power prediction; Radial basis function; Wind power generation;

D O I：

10.3969/j.issn.1000-1026.2012.05.005

中图分类号：

学科分类号：

摘要：

It is very important to forecast short-term wind farm output for the security and stability of the power grid. Due to its non-steady and non-periodic characteristic, the wind power time series is decomposed into the random component and trend component by using the empirical mode decomposition (EMD) theory. Chaotic prediction is made for the random components and trend components using neural network with radical basis function and using least squares support vector machine, respectively, thus the final consequence can be obtained by combining the prediction result of each component. The power output of a wind farm in Yunnan is used as the case study for the model proposed. The outcome shows that the prediction model has high accuracy compared with the traditional artificial neural prediction model and provides a reference for the wind power forecasting. © 2012 State Grid Electric Power Research Institute Press.

引用

页码：24 / 28

页数：4

共 15 条

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