基于IAOA-VMD-LSTM的超短期风电功率预测

肖烈禧; 张玉; 周辉; 赵冠皓

doi:10.19912/j.0254-0096.tynxb.2022-1054

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (11) : 239-246. DOI: 10.19912/j.0254-0096.tynxb.2022-1054

基于IAOA-VMD-LSTM的超短期风电功率预测

肖烈禧¹, 张玉^{1, 2}, 周辉¹, 赵冠皓¹

作者信息 +

ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM

Xiao Liexi¹, Zhang Yu^1,2, Zhou Hui¹, Zhao Guanhao¹

Author information +

文章历史 +

摘要

为了对风电功率进行精确预测,提出一种基于改进算术优化算法（IAOA）、变分模态分解（VMD）和长短期记忆网络（LSTM）的超短期风电功率预测模型（IAOA-VMD-LSTM）。利用IAOA对VMD的关键分解参数k和α进行优化,得到的各固有模态函数（IMF）具有周期性,能够提升LSTM的预测精度,同时利用IAOA对LSTM网络参数进行优化。通过对风电功率数据进行预测分析,结果表明IAOA-VMD-LSTM预测模型相比于其他模型的预测精度更高。

Abstract

In order to accurately predict wind power, an ultra-short-term wind power prediction model was proposed based on improved arithmetic optimization algorithm （IAOA）, variational modal decomposition （VMD） and long short-term memory network （LSTM）. The IAOA algorithm was used to optimize the key decomposition parameters k and α of VMD, and the inherent modal functions （IMF） obtained were periodic, which could improve the prediction accuracy of LSTM. Meanwhile, the IAOA algorithm was used to optimize the LSTM network parameters. Through the prediction analysis of wind power data, the results show that the IAOA-VMD-LSTM prediction model has higher prediction accuracy than other models.

导出引用

肖烈禧, 张玉, 周辉, 赵冠皓. 基于IAOA-VMD-LSTM的超短期风电功率预测[J]. 太阳能学报. 2023, 44(11): 239-246 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1054

Xiao Liexi, Zhang Yu, Zhou Hui, Zhao Guanhao. ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(11): 239-246 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1054

中图分类号： TM614

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