基于CPO-VMD-FE改进功率序列及NRBO优化时序预测模型的风电功率短期预测

黄曌; 杨渊文; 王欣; 郭智薇; 张柳

doi:10.19912/j.0254-0096.tynxb.2024-2345

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (5) : 268-277. DOI: 10.19912/j.0254-0096.tynxb.2024-2345

基于CPO-VMD-FE改进功率序列及NRBO优化时序预测模型的风电功率短期预测

黄曌¹, 杨渊文^1,2, 王欣², 郭智薇¹, 张柳¹

作者信息 +

SHORT-TERM WIND POWER PREDICTION BY CPO-VMD-FE REFINED POWER SERIES AND NRBO OPTIMIZED LEARNING MODEL

Huang Zhao¹, Yang Yuanwen^1,2, Wang Xin², Guo Zhiwei¹, Zhang Liu¹

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文章历史 +

摘要

针对气象数据缺失导致的风电功率预测精度不足问题,提出一种融合冠豪猪优化变分模态分解（CPO-VMD）与牛顿-拉夫逊优化含注意力机制长短期记忆网络（NRBO-LSTM-Attention）的组合模型。首先,通过CPO算法自适应确定VMD参数,将原始功率序列分解,基于模糊熵（FE）重构相似分量,降低数据复杂度。之后,利用NRBO优化LSTM-Attention网络参数,强化模型对时序特征的动态捕捉能力。仿真表明,该模型在气象数据缺失场景下能获得理想的预测精度,为复杂环境下的风电稳定调度提供可靠技术支持。

Abstract

To address the insufficient accuracy of wind power forecasting caused by missing meteorological data, a hybrid model combining crested porcupine optimizer-variational modal decomposition （CPO-VMD） and Newton-Raphson based optimizer-long short term memory-attention mechanism （NRBO-LSTM-Attention） is proposed. First, the CPO algorithm adaptively optimizes VMD parameters to decompose the raw power series, and similar components are reconstructed based on fuzzy entropy （FE） to reduce data complexity. Then, the NRBO algorithm is used to optimize the parameters of the LSTM-Attention network, enhancing the dynamic capture capability of temporal features. Simulation results show that the model achieves ideal prediction accuracy in under missing meteorological data, providing reliable technical support for wind power dispatch in complex environments.

导出引用

黄曌, 杨渊文, 王欣, 郭智薇, 张柳. 基于CPO-VMD-FE改进功率序列及NRBO优化时序预测模型的风电功率短期预测[J]. 太阳能学报. 2026, 47(5): 268-277 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2345

Huang Zhao, Yang Yuanwen, Wang Xin, Guo Zhiwei, Zhang Liu. SHORT-TERM WIND POWER PREDICTION BY CPO-VMD-FE REFINED POWER SERIES AND NRBO OPTIMIZED LEARNING MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 268-277 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2345

中图分类号： TM614

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