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
- 基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法
- 太阳能学报 2026年47卷第9期页码：805-812
- 作者机构：
- 作者简介：
- 基金信息：
  
  浙江省自然科学基金（LBMHQN26E060001）;国家自然科学基金（52375135;52305137）
- DOI：10.19912/j.0254-0096.tynxb.2025-0773
  中图分类号： TM614
- 网络首发：2026-10-10，
  
  纸质出版：2026
- 稿件说明：
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周家璇, 顾希雯, 梅益铭, 等. 基于PM-iTransformer-TCN和贝叶斯优化的超短期风电功率预测方法[J]. 太阳能学报, 2026,47(9):805-812.

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

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

references

朱国鹏, 向玲, 范文振, 等. 基于样本卷积交互网络的风电场集群短期功率预测[J]. 太阳能学报, 2025, 46(1): 158-167.

Li K, Shen R F, Wang Z G, et al.An efficient wind speed prediction method based on a deep neural network without future information leakage[J]. Energy, 2023, 267: 126589.

宋江涛, 崔双喜, 刘洪广. 基于二次分解NGO-VMD残差项与长短时记忆神经网络的超短期风功率预测[J]. 科学技术与工程, 2023, 23(6): 2428-2437.

Chen P Y, Pedersen T, Bak-Jensen B, et al.ARIMA-based time series model of stochastic wind power generation[J]. IEEE Transactions on Power Systems, 2010, 25(2): 667-676.

Cong Z H, Yu Y C, Li L Y, et al.Wind power time series simulation model based on typical daily output processes and Markov algorithm[J]. Global Energy Interconnection, 2022, 5(1): 44-54.

彭小圣, 熊磊, 文劲宇, 等. 风电集群短期及超短期功率预测精度改进方法综述[J]. 中国电机工程学报, 2016, 36(23): 6315-6326, 6596.

付振宇, 王文胤, 凌小明, 等. 基于长短期记忆及自适应Kriging子集模拟优化的风电功率预测方法[J]. 电网与清洁能源, 2024, 40(11): 147-154.

宋江涛, 崔双喜, 樊小朝, 等. 基于SGMD-SE与优化TCN-BiLSTM/BiGRU的超短期风功率预测[J]. 太阳能学报, 2024, 45(10): 588-596.

韩宇超, 同向前, 邓亚平. 基于概率密度估计与时序Transformer网络的风功率日前区间预测[J]. 中国电机工程学报, 2024, 44(23): 9285-9296.

Li N, Dong J, Liu L Y, et al.A novel EMD and causal convolutional network integrated with Transformer for ultra short-term wind power forecasting[J]. International journal of Electrical Power & Energy Systems, 2023, 154: 109470.

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.

Yang S X, Zhou J X, Gu X W, et al.A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application[J]. Energy, 2024, 313: 133911.

Chen Y S, Yu S, Islam S, et al.Decomposition-based wind power forecasting models and their boundary issue: an in-depth review and comprehensive discussion on potential solutions[J]. Energy Reports, 2022, 8: 8805-8820.

许梦田, 王洪哲, 赵成萍, 等. 基于短期风功率预测的数据预处理算法研究[J]. 可再生能源, 2019, 37(1): 119-125.

Zhao Y W, Shi X H, Zhou B X, et al.Wind power fluctuation smoothing with BESS considering ultra-short-term prediction[C]//2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). Chengdu, China, 2019: 4025-4030.

Qu Z J, Hou X X, Li J, et al.Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation[J]. Energy, 2024, 290: 130155.

Wang J J, Tang X D.An intensive decomposition integration paradigm for short-term wind power forecasting based on feature extraction and optimal weighted combination strategy[J]. Measurement, 2023, 223: 113811.

Guo J B, He C J, Zhang X T.Nonlinear edge-preserving diffusion with adaptive source for document images binarization[J]. Applied Mathematics and Computation, 2019, 351: 8-22.

Liu Y, Hu T G, Zhang H R, et al.iTransformer: inverted transformers are effective for time series forecasting[C]//International Conference on Learning Representations (ICLR). Vienna, Austria, 2024: 11116-11140.

Lea C, Flynn M D, Vidal R, et al.Temporal convolutional networks for action segmentation and detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017: 1003-1012.

冯易鑫, 彭辉, 罗威. 聚类分析-神经网络-贝叶斯优化联合识别复合材料参数研究[J]. 力学学报, 2024, 56(11): 3333-3350.

Fan H J, Zhen Z, Liu N, et al.Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method[J]. Energy, 2023, 266: 126420.

Banik A, Behera C, Sarathkumar T V, et al.Uncertain wind power forecasting using LSTM-based prediction interval[J]. IET Renewable Power Generation, 2020, 14(14): 2657-2667.

Zha W T, Liu J, Li Y L, et al.Ultra-short-term power forecast method for the wind farm based on feature selection and temporal convolution network[J]. ISA Transactions, 2022, 129: 405-414.

Dai X R, Liu G P, Hu W S.An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting[J]. Energy, 2023, 272: 127173.

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