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

江善和; 徐小艳; 涂亮; 陈文胜

doi:doi:10.19912/j.0254-0096.tynxb.2024-1602

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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

更新时间：2026-02-06

- 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
- Vol. 47, Issue 1, Pages: 593-603(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1602
  CLC： TM614
- Online First：05 February 2026，
  
  Published：05 February 2026
- 稿件说明：
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江善和, 徐小艳, 涂亮, et al. 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[J]. 2026, 47(1): 593-603.
DOI：

江善和, 徐小艳, 涂亮, et al. 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[J]. 2026, 47(1): 593-603. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1602.

摘要

针对风电场数据具有复杂的非线性关系

且难以捕捉长距离依赖关系的问题

引入时间段分割策略

提出一种新的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（PBY-Trans）。该方法采用时间段分割技术分割风电场数据为子序列

将其作为Transformer模型编码器的输入

从而更好地适应时间序列的非线性特征;使用贝叶斯算法搜索Transformer模型参数的最优配置

实现模型性能提升

进一步提高预测准确度。采用Bengaluru某风电场数据集对所提方法的预测性能进行比较验证

相较于SVM、RNN、Informer、LSTM、GRU和TCN模型

所提PBY-Trans方法在平均绝对误差（MAE）分别下降33.56%、59.75%、47.27%、32.34%、40.46%和27.71%

均方根误差（RMSE）分别下降68.99%、37.05%、27.60%、14.43%、16.42%和12.29%。结果表明

该文提出的Transform时间段分割模型的风电功率预测方法能够进一步提高预测精度

鲁棒性能得到有效提升。

Abstract

Aiming at the complex nonlinear relationship and the difficulty in capturing long-range dependencies of wind farm data

a new ultra-short-term wind power prediction method （PBY-Trans） based on a Bayesian parameter-optimized Transformer time segmenting model is proposed by introducing a time segmentation strategy. This method employs the time segmentation technique to divide the wind farm data into subsequences

which are then utilized as the inputs to the Transformer model encoder to better adapt to the nonlinear characteristics of the time series. Furthermore

a Bayesian algorithm is employed to search for the optimal configuration of the Transformer model parameters

thereby enhancing the model performance and improving the prediction accuracy. The prediction performance of the proposed method is compared and verified using the data set of a wind farm in Bengaluru. Compared with the SVM

RNN

Informer

LSTM

GRU and TCN models

the mean absolute error（MAE） metric of the proposed PBY-Trans method achieved a decrease of 33.56%

59.75%

47.27%

32.34%

40.46% and 27.71%

and the root mean square error（RMSE） was also reduced by 68.99%

37.05%

27.60%

14.43%

16.42% and 12.29%

respectively. The results indicate that the proposed PBY-Trans prediction model can further enhance prediction accuracy and the robustness can be effectively enhanced.

关键词

Keywords

references

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