优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测

杨渊文; 黄曌; 王欣; 郭智薇; 张柳

doi:doi:10.19912/j.0254-0096.tynxb.2024-1492

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优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测

更新时间：2026-02-06

- 优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测
- Vol. 47, Issue 1, Pages: 441-449(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1492
  CLC： TM614
- Online First：05 February 2026，
  
  Published：05 February 2026
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杨渊文, 黄曌, 王欣, et al. 优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测[J]. 2026, 47(1): 441-449.
DOI：

杨渊文, 黄曌, 王欣, et al. 优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测[J]. 2026, 47(1): 441-449. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1492.

摘要

为提高数值天气预报（NWP）预测风速的精确性

将NWP风速与实际风电场风速输入到全局搜索策略鲸鱼算法（GSWOA）优化的变分模态分解（VMD）进行分解。分解后的实际风速分量作为训练目标

对应的NWP风速分量则输入基于牛顿-拉夫逊优化算法-长短期记忆网络加注意力机制（NRBO-LSTM-Attention）模型

将输出的各分量线性叠加后替换原NWP风速。之后

通过孤立森林和Ransac算法等对修正后的NWP与风电场数据进行异常值清洗

最终输入NRBO-LSTM-Attention模型

用于预测未来功率。仿真结果表明：修正后的NWP风速更接近实际风速

评估指标平均绝对误差（MAE）和均方根误差（RMSE）分别降低11.45%和19.82%

R2提升31.24%;预测功率模型的性能更优

MAE和RMSE分别降低11.36%和10.43%

R2提升3.42%。

Abstract

To enhance the accuracy of wind speed prediction in numerical weather prediction （NWP）

both NWP-derived wind speeds and actual wind farm wind speeds are input into a variational mode decomposition （VMD） optimized by the global search strategy whale optimization algorithm （GSWOA） for decomposition. The decomposed actual wind speed components serve as training targets

while the corresponding NWP wind speed components are fed into the Newton-Raphson-based optimizer-long short-term memory with attention mechanism （NRBO-LSTM-Attention） model. The output components are linearly superimposed to replace the original NWP wind speeds. Subsequently

the corrected NWP data and wind farm data undergo outlier cleaning using algorithms such as isolation forest and Ransac. The processed data is finally input into the NRBO-LSTM-Attention model to predict future power output. Simulation results demonstrate that the corrected NWP wind speeds are closer to actual wind speeds

with evaluation metrics MAE and RMSE decreasing by 11.45% and 19.82%

respectively

and increasing by 31.24%. Additionally

the power prediction model exhibits superior performance

with MAE and RMSE reduced by 11.36% and 10.43%

respectively

and improved by 3.42%.

关键词

Keywords

references

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