基于改进鱼鹰优化算法与VMD-LSTM的超短期风电功率预测

罗潇远; 刘杰; 杨斌; 覃涛; 陈昌盛; 杨靖

doi:doi:10.19912/j.0254-0096.tynxb.2023-1942

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基于改进鱼鹰优化算法与VMD-LSTM的超短期风电功率预测

更新时间：2025-10-18

- 基于改进鱼鹰优化算法与VMD-LSTM的超短期风电功率预测
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- 太阳能学报 2025年46卷第3期页码：652-660
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- DOI：doi:10.19912/j.0254-0096.tynxb.2023-1942
  中图分类号： TM614
- 网络出版：2025-04-07，
  
  纸质出版：2025
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罗潇远, 刘杰, 杨斌, 等. 基于改进鱼鹰优化算法与VMD-LSTM的超短期风电功率预测[J]. 太阳能学报, 2025,46(3):652-660.
罗潇远, 刘杰, 杨斌, 等. 基于改进鱼鹰优化算法与VMD-LSTM的超短期风电功率预测[J]. 太阳能学报, 2025,46(3):652-660. DOI： doi:10.19912/j.0254-0096.tynxb.2023-1942.

DOI：

摘要

为提升超短期风电功率的预测精度

提出一种加入融合柯西变异和反向学习策略的改进鱼鹰优化算法（IOOA）

用于优化以长短期记忆网络（LSTM）和变模态分解（VMD）为基础的组合预测模型。首先

采用变模态分解收集的历史风电功率数据

将非线性较强的原始功率数据分解为较为稳定的子序列。其次

使用改进鱼鹰优化算法对长短期记忆网络的隐藏单元数目、训练周期、初始学习率3个参数进行寻优。最后

使用长短期记忆网络对各子序列预测

将各子序列预测值叠加起来得到最终结果。通过风电场实测数据仿真分析

相比于普通长短期记忆网络模型的预测结果

所提模型的均方根误差下降了62.5%、平均绝对百分比误差和平均绝对误差分别下降了61.1%和55.9%

预测精度也高于其他4种组合预测模型

表明该模型成功提高了超短期风电功率的预测精度。

Abstract

To enhance the accuracy of ultra-short-term wind power

a new optimization algorithm called improved osprey optimization algorithm （IOOA） is proposed. It combines Cauchy mutation and reverse learning strategy to optimize a combination prediction model based on long short-term memory network （LSTM） and variable mode decomposition （VMD）. Firstly

the historical wind power data collected through VMD is used to decompose the original power data with strong nonlinearity into relatively stable subsequences. Secondly

the osprey optimization algorithm

combining Cauchy mutation and reverse learning strategies

is used to optimize the number of hidden units

training period

and initial learning rate of the LSTM. Finally

the final prediction result is obtained by using a LSTM to predict each subsequence

and the predicted values of each subsequence were superimposed to obtain the final result.The proposed wind farm prediction model has been analyzed by using measured data. The results were compared with ordinary short-term memory neural network models. The proposed model reduced the RMSE by 62.5%

decreased MAPE by 62.2% and MAE by 55.9%. And the prediction accuracy is also higher than other four combination prediction models

indicating its success in improving the prediction accuracy of short-term wind power.

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Keywords

references

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