Super short-term prediction of wind power based on EEMD-PSO-ELM

MAO Yuan; FENG Yang; YAN Yan; CHEN Lei; QIAN Yong

doi:10.3969/j.issn.1672-3643.2024.02.001

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Super short-term prediction of wind power based on EEMD-PSO-ELM

更新时间：2026-06-27

- Super short-term prediction of wind power based on EEMD-PSO-ELM
- Ningxia Electric Power Issue 2, (2024)
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- DOI：10.3969/j.issn.1672-3643.2024.02.001
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- Online First：27 May 2024，
  
  Published：2024
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MAO Yuan, FENG Yang, YAN Yan, et al. Super short-term prediction of wind power based on EEMD-PSO-ELM[J]. Ningxia Electric Power, 2024, (2).
DOI：

MAO Yuan, FENG Yang, YAN Yan, et al. Super short-term prediction of wind power based on EEMD-PSO-ELM[J]. Ningxia Electric Power, 2024, (2). DOI： 10.3969/j.issn.1672-3643.2024.02.001.

摘要

针对风电场功率不稳定特性引起风电功率预测精度不高的问题，提出1种基于EEMD-PSO-ELM的超短期风电功率预测方法。首先，采用集合经验模态分解(ensemble empirical mode decomposition

EEMD)将风电功率序列分解为若干个模态，从而避免了模态混叠；其次，利用相空间重构对分解得到的模态计算Hurst指数，并依据Hurst指数得到最优子序列；最后，采用粒子群算法(particle swarm optimization

PSO)-极限学习机(extreme learning machine

ELM)模型对最优子序列风电功率进行预测。以某风电场为例，采用预测模型进行分析，实验结果表明EEMD-PSO-ELM预测模型的风电功率预测精度更高。

Abstract

Addressing the problem of low wind power prediction accuracy caused by the unstable characteristics of wind farm power

a super-short-term wind power prediction method based on ensemble empirical mode decomposition(EEMD)

particle swarm optimization(PSO)

and extreme learning machine(ELM)is proposed.Firstly

the wind power sequence is decomposed into several modes using EEMD to avoid mode aliasing.Secondly

phase space reconstruction is used to calculate the Hurst exponent for the decomposed modes

and the optimal sub-sequence is obtained according to the Hurst exponent.Finally

the PSO-ELM model predicts the wind power for the optimal sub-sequence.Experimental results from a specific wind farm illustrate that the EEMD-PSO-ELM prediction model achieves higher accuracy in wind power forecasting.

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