基于集成分解与多头注意力机制的风电功率区间预测

李国民; 张君; 赵春阳; 李庚银; 张亚刚

doi:10.19912/j.0254-0096.tynxb.2024-2441

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

基于集成分解与多头注意力机制的风电功率区间预测

更新时间：2026-07-21

- 基于集成分解与多头注意力机制的风电功率区间预测
- Vol. 47, Issue 5, Pages: 332-340(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2024-2441
  CLC： TM614
- Online First：04 June 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
李国民, 张君, 赵春阳, et al. 基于集成分解与多头注意力机制的风电功率区间预测[J]. 2026, 47(5): 332-340.
DOI：

李国民, 张君, 赵春阳, et al. 基于集成分解与多头注意力机制的风电功率区间预测[J]. 2026, 47(5): 332-340. DOI： 10.19912/j.0254-0096.tynxb.2024-2441.

摘要

针对传统风电功率预测方法在处理复杂信号时的局限性

提出一种结合集成分解与多模型融合的风电功率预测新方法。首先

采用融合混合策略的混沌-莱维黏菌（SCL）算法优化改进的完全自适应噪声集合经验模态分解方法（ICEEMDAN）

应用于风电功率数据的频率分解

将信号分解为高频、中频和低频分量。针对不同频率的分量

分别应用Transformer、TCN-BIGRU和OSELM进行单独建模

以充分捕捉不同频率分量的时序特征和非线性规律。随后利用多头注意力机制对各频率分量的预测结果进行加权融合

充分考虑不同分量对风电功率预测的贡献。为获得更具鲁棒性的风电功率预测结果

利用改进的马尔可夫链蒙特卡洛（IMCMC）方法对预测结果进行区间预测。实验结果表明

所提出的方法相比于传统单一模型预测方法

R2分别提高了14.2% 与7.15%

在95%置信区间PICP分别为95.78%与95.93%。

Abstract

Aiming at the limitations of traditional wind power prediction methods in dealing with complex signals

a new method for wind power prediction combining integrated decomposition and multi-model fusion is proposed. First

a Slime Chaos Levy （SCL） algorithm incorporating a hybrid strategy is proposed for optimizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （ICEEMDAN） method

which is applied to the frequency decomposition of wind power data to decompose the signals into high

medium and low frequency components. Transformer

temporal convolutional network and bidirectional gated recurrent unit （TCN-BIGRU）

and online sequential extreme learning machine （OSELM） are applied to represent different frequency components in order to completely capture their temporal features and nonlinear patterns. Subsequently

to fully account for the contribution of various components to the wind power forecast

the prediction results of each frequency component are then weighted and fused using the multi-head attention mechanism. In order to obtain more robust wind power prediction results

the improved Markov chain Monte Carlo （IMCMC） method is utilized to predict the prediction results in intervals. The experimental findings demonstrate that the target method increases the by 14.2% and 7.15%

respectively

and the PICP is 95.78% and 95.93% at 95% confidence intervals

respectively

when compared to the typical single-model prediction method.

关键词

Keywords

references

冯双磊, 王伟胜, 刘纯, 等. 风电场功率预测物理方法研究[J]. 中国电机工程学报, 2010, 30(2): 1-6.

YUNUS K, THIRINGER T, CHEN P Y.ARIMA-based frequency-decomposed modeling of wind speed time series[J]. IEEE transactions on power systems, 2016, 31(4): 2546-2556.

TRIZOGLOU P, LIU X L, LIN Z.Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines[J]. Renewable energy, 2021, 179: 945-962.

马志侠, 张林鍹, 巴音塔娜, 等. 基于自适应二次分解与CNN-BiLSTM的超短期风电功率预测[J]. 太阳能学报, 2024, 45(6): 429-435.

钟吴君, 李培强, 涂春鸣. 基于尾流关联的动态超图风电功率超短期预测方法[J]. 中国电机工程学报, 2025, 45(12): 4693-4706.

徐武, 范鑫豪, 沈智方, 等. 多尺度特征提取的Transformer短期风电功率预测[J]. 太阳能学报, 2025, 46(2): 640-648.

赵卿, 高文华, 石慧, 等. 基于WT和黏菌算法的LSSVM短期风功率预测[J]. 计算机仿真, 2024, 41(7): 166-170, 226.

陈运蓬, 景超, 白静波, 等. 基于集成学习的新能源发电功率预测[J]. 太阳能学报, 2024, 45(6): 412-421.

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.

谷学静, 陈洪磊, 孙泽贤, 等. 基于VMD-RL-LSTM的短期风功率预测[J]. 计算机仿真, 2023, 40(4): 89-93, 309.

陈子含, 滕伟, 胥学峰, 等. 基于图卷积网络和风速差分拟合的中长期风功率预测[J]. 中国电力, 2023, 56(10): 96-105.

荆志宇, 李培强, 林文婷. 结合贝叶斯优化及通道注意力的双端优化时序式风功率预测模型[J]. 电力系统及其自动化学报, 2024, 36(8): 39-47, 59.

LI S M, CHEN H L, WANG M J, et al.Slime mould algorithm: a new method for stochastic optimization[J]. Future generation computer systems, 2020, 111: 300-323.

GEISEL T, FAIREN V.Statistical properties of chaos in Chebyshev maps[J]. Physics letters A, 1984, 105(6): 263-266.

程彦琳, 李书琴. 基于混沌映射和莱维飞行扰动的蛇形优化算法[J]. 计算机工程与设计, 2024, 45(9): 2658-2668.

BOMMIDI B S, TEEPARTHI K, KOSANA V.Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function[J]. Energy, 2023, 265: 126383.

YU D J, CHENG J S, YANG Y.Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical systems and signal processing, 2005, 19(2): 259-270.

VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.

ZHU R J, LIAO W L, WANG Y S.Short-term prediction for wind power based on temporal convolutional network[J]. Energy reports, 2020, 6: 424-429.

盛四清, 金航, 刘长荣. 基于VMD-WSGRU的风电场发电功率中短期及短期预测[J]. 电网技术, 2022, 46(3): 897-904.

XU M J, WANG K, LI M, et al.An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine[J]. Aerospace science and technology, 2023, 141: 108513.

ZHANG Y G, ZHAO Y P, SHEN X Y, et al.A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms[J]. Applied energy, 2022, 305: 117815.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

SHORT-TERM WIND POWER INTERVAL PREDICTION BASED ON MIXED QUANTILE REGRESSION LONG AND SHORT-TERM MEMORYNEURAL NETWORK

基于平均功率估算的海上漂浮式光伏MPPT研究

基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测

基于贝叶斯参数优化Transformer时间段分割模型的超短期风电功率预测方法

基于改进MOGRO的港区配电网无功优化策略研究

Related Author

Yang Mao

Zhang Shutian

Wang Bo

Yu Xinnan

陈继明

郭瑾

刘静

黄磊

Related Institution

State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute

Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University

The College of Electrical and Information Engineering, Lanzhou University of Technology

Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education （Northeast Electric Power University）

School of Mechanical and Electrical Engineering, Lanzhou University of Technology

AI问答

⁰