基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法

周家璇; 顾希雯; 梅益铭; 杨世锡; 单宗俊

doi:10.19912/j.0254-0096.tynxb.2025-0773

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基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法

更新时间：2026-10-15

- 基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法
- Vol. 47, Issue 9, Pages: 805-812(2026)
- 作者机构：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0773
  CLC： TM614
- Online First：10 October 2026，
  
  Published：2026
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周家璇, 顾希雯, 梅益铭, et al. 基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法[J]. 2026, 47(9): 805-812.
DOI：

周家璇, 顾希雯, 梅益铭, et al. 基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法[J]. 2026, 47(9): 805-812. DOI： 10.19912/j.0254-0096.tynxb.2025-0773.

摘要

针对传统机器学习方法难以应对风电功率的强波动特性、预测精度不足的问题

提出一种基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法。首先通过Perona-Malik算法对原始风电功率序列进行平滑预处理

抑制其中的随机性成分;其次通过iTransformer模型联合风速、风向、温度、气压等多组输入变量

获得风电功率的初步预测值;最后利用时间卷积网络（TCN）基于风电功率数据的波动特性

实现多步预测误差的实时追踪和补偿。将贝叶斯优化（BO）应用于训练过程

以实现最优参数组合的选取。采用中国甘肃某风电场的现场数据进行方法验证。将所提方法与多种基线机器学习模型进行对比

结果表明

所提方法在未来2 h和4 h预测的归一化均方根误差（NRMSE）分别为18.95%和21.64%

优于传统的机器学习方法

可显著提升超短期风电功率预测的准确性。

Abstract

A multi-step ultra-short-term wind power forecasting method based on PM-iTransformer-TCN and Bayesian optimization （BO） is proposed to address the limitations of traditional machine learning methods

which struggle with the strong fluctuations of wind power and demonstrate insufficient prediction accuracy. First

the original wind power data is preprocessed using the Perona-Malik algorithm to suppress random components. Next

the iTransformer model combines multiple input variables

including wind speed

wind direction

temperature

and air pressure

to generate preliminary predicted values of wind power. Finally

real-time tracking and correction of multi-step prediction errors are achieved using Temporal Convolutional Networks （TCNs）

tailored to the fluctuation characteristics of wind power data. BO is applied in the training process to select the optimal parameter combination. The proposed method is compared with several baseline machine learning models using on-site data from a wind farm in Gansu Province

China. The results indicate that the normalized root mean square errors of the forecasting results at future 2 h and 4 h are 18.95% and 21.64%

respectively

outperforming traditional machine learning methods and significantly enhancing the accuracy of ultra-short-term wind power forecasting.

关键词

Keywords

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