Analysis and prediction modeling of wind power characteristics based on EMD decomposition

被引：0

作者：

Wen X. ^{[1
,2
]}

Xu Y. ^{[1
]}

机构：

[1] College of Automation Engineering, Northeast Electric Power University, Jilin

[2] Jilin Province International Research Center of Precision Drive and Intelligent Control, Jilin

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 11期

关键词：

Ant colony optimization; Empirical mode decomposition; Models; Neural networks; Prediction; Wind power;

D O I：

10.19912/j.0254-0096.tynxb.2019-1272

中图分类号：

学科分类号：

摘要：

In this paper, the time series of wind power(TSWP) is decomposed into multiple eigenmode functions by empirical mode decomposition(EMD) based on the actual wind power data. The local time-frequency characteristics of each component of the TSWP are judged, and the new TSWP is reconstructed by fractal theory. Then, the chaos characteristics of the TSWP are analyzed by using the maximum Lyapunov index and other characteristics, and the behavior dynamics characteristics of the 3 scale subsequences are analyzed respectively. Finally, the prediction model of the TSWP is built based on the ant colony optimized extreme learning mechanism. The simulation results shown that the proposed model is more accurate than other single forecasting models, which can be used in engineering practice. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：293 / 298

页数：5

共 13 条

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