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

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2026-07-21

- 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
- Vol. 47, Issue 5, Pages: 268-277(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2024-2345
  CLC： TM614
- Online First：04 June 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
黄曌, 杨渊文, 王欣, et al. 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]. 2026, 47(5): 268-277.
DOI：

黄曌, 杨渊文, 王欣, et al. 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]. 2026, 47(5): 268-277. DOI： 10.19912/j.0254-0096.tynxb.2024-2345.

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.

关键词

Keywords

references

赵传, 戴朝华, 付洋, 等. 考虑风电预测误差与系统安全域的风电装机规划多目标优化方法[J]. 太阳能学报, 2020, 41(2): 110-117.

MCKENNA R, LILLIESTAM J, HEINRICHS H U, et al.System impacts of wind energy developments: key research challenges and opportunities[J]. Joule, 2025, 9(1): 101799.

赵泽妮, 云斯宁, 贾凌云, 等. 基于统计模型的短期风能预测方法研究进展[J]. 太阳能学报, 2022, 43(11): 224-234.

舒印彪, 陈国平, 贺静波, 等. 构建以新能源为主体的新型电力系统框架研究[J]. 中国工程科学, 2021, 23(6): 61-69.

戴剑丰, 王子博, 谢嫦嫦, 等. 考虑大规模风电接入的电网薄弱区域动态无功需求评估[J]. 太阳能学报, 2024, 45(9): 60-69.

陈海鹏, 李赫, 阚天洋, 等. 考虑风电时序特性的深度小波-时序卷积网络超短期风功率预测[J]. 电网技术, 2023, 47(4): 1653-1662.

WU B R, WANG L, ZENG Y R.Interpretable wind speed prediction with multivariate time series and temporal fusion transformers[J]. Energy, 2022, 252: 123990.

张昊立, 张菁, 倪建辉, 等. 引入注意力机制的LSTM-FCN海上风电功率预测[J]. 太阳能学报, 2024, 45(6): 444-450.

WANG S X, SHI J R, YANG W, et al.High and low frequency wind power prediction based on Transformer and BiGRU-Attention[J]. Energy, 2024, 288: 129753.

ZHAO H, HUANG X Q, XIAO Z N, et al.Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks[J]. Renewable energy, 2024, 220: 119706.

CAO W, MENG Z, LI J M, et al.A remaining useful life prediction method for rolling bearing based on TCN-transformer[J]. IEEE transactions on instrumentation and measurement, 2025, 74: 3501309.

贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.

SOWMYA R, PREMKUMAR M, JANGIR P.Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems[J]. Engineering applications of artificial intelligence, 2024, 128: 107532.

SHI Y F, YANG C, WANG J, et al.A near-real-time forecasting model of high-frequency radiowave propagation factor fusion based on the ICEEMDAN decomposition and Bi-LSTM methods[J]. IEEE transactions on antennas and propagation, 2024, 72(7): 6032-6044.

ZHOU Y, ZHU X X.Forecasting USD/RMB exchange rate using the ICEEMDAN-CNN-LSTM model[J]. Journal of forecasting, 2025, 44(1): 200-215.

ZHANG Y G, PAN G F, CHEN B, et al.Short-term wind speed prediction model based on GA-ANN improved by VMD[J]. Renewable energy, 2020, 156: 1373-1388.

YU M, NIU D X, GAO T, et al.A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism[J]. Energy, 2023, 269: 126738.

ABDEL-BASSET M, MOHAMED R, ABOUHAWWASH M.Crested porcupine optimizer: a new nature-inspired metaheuristic[J]. Knowledge-based systems, 2024, 284: 111257.

LI S J, FAN Z Y.Evaluation of urban green space landscape planning scheme based on PSO-BP neural network model[J]. Alexandria engineering journal, 2022, 61(9): 7141-7153.

刘吉成, 朱玺瑞, 于晶, 等. 基于IWOA-SA-Elman神经网络的短期风电功率预测[J]. 太阳能学报, 2024, 45(1): 143-150.

DONG K, RAN P, FAN Q Y, et al.Comparison of improved hybrid FTS models for forecasting the urban air quality index[J]. Journal of cleaner production, 2023, 428: 139234.

赵妍, 潘怡, 李亚波, 等. 基于AVMD多尺度模糊熵和VPMCD算法的宽频振荡分类[J]. 电力系统保护与控制, 2024, 52(13): 179-187.

贾象阳, 黄先锋, 牛文渊, 等. 顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用[J]. 测绘学报, 2021, 50(2): 226-234.

李丽芬, 陈旭, 曹旺斌, 等. 基于注意力特征融合时空图网络的超短期风电功率预测[J]. 电力科学与工程, 2024, 40(10): 19-29.

SHAHID F, ZAMEER A, MUNEEB M.A novel genetic LSTM model for wind power forecast[J]. Energy, 2021, 223: 120069.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

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

基于TCN-NCP-CFC神经网络的超短期风电功率预测

基于BIRCH-MSSCN模型的风电叶片结冰故障预警

基于黑翅鸢算法和VMD的短期风电功率预测

基于VMD-MBWO-KELM的短期风电功率预测

Related Author

赵飞

吴文标

汪燕毅

李练兵

雒威

程晴

苏文勇

陈业策

Related Institution

School of Electrical and Information Engineering, Jiangsu University

Jiangsu Zhen An Power Equipment Co., Ltd.

School of Computer Science and Technology, Shanghai University of Electric Power

Electric Power Research Institute of State Grid Shanghai Electric Power Company

Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology, School of Energy and Environment, Southeast University

AI问答

⁰