Short-term wind power forecasting based on adaptive decomposition and frequency-domain enhanced iTransformer

LIU Xinyu; WANG Zhen; WANG Silei; YANG Songkun; LAN Yuxiang; WANG Saisai

doi:10.19783/j.cnki.pspc.260045

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Short-term wind power forecasting based on adaptive decomposition and frequency-domain enhanced iTransformer

更新时间：2026-07-27

- Short-term wind power forecasting based on adaptive decomposition and frequency-domain enhanced iTransformer
- Power System Protection and Control Vol. 54, Issue 13, (2026)
- 作者机构：
  
  华北水利水电大学电气工程学院,河南,郑州,450045
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260045
  CLC：
- Published：2026
- 稿件说明：
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LIU Xinyu, WANG Zhen, WANG Silei, et al. Short-term wind power forecasting based on adaptive decomposition and frequency-domain enhanced iTransformer[J]. Power System Protection and Control, 2026, 54(13).
DOI：

LIU Xinyu, WANG Zhen, WANG Silei, et al. Short-term wind power forecasting based on adaptive decomposition and frequency-domain enhanced iTransformer[J]. Power System Protection and Control, 2026, 54(13). DOI： 10.19783/j.cnki.pspc.260045.

摘要

为提高风电功率预测在复杂波动、多变量和强周期条件下的准确性，提出一种融合自适应信号分解与频域增强iTransformer的预测方法。首先，利用霜冰优化算法(rime ice optimization algorithm

RIME)对变分模态分解(variational mode decomposition

VMD)的模态数与惩罚因子进行自适应寻优，对原始风电功率序列进行分解。随后引入样本熵(sample entropy

SE)对分解得到的模态分量进行重构，以降低输入维度并抑制噪声干扰。在预测阶段，构建基于iTransformer的iFANformer模型，通过以傅里叶分析网络(Fourier analysis networks

FAN)替代前馈结构，引入可学习频率特征以增强对序列周期特征的刻画能力。最后，基于实际风电场运行数据开展对比实验与消融分析。结果表明，所提方法在预测精度上优于对比模型，验证了自适应分解与频域增强策略的有效性。

Abstract

To improve wind power forecasting accuracy under complex fluctuation

multivariable coupling

and strong periodicity conditions

this paper proposes a forecasting method integrating adaptive signal decomposition and a frequency-domain enhanced iTransformer. First

the rime ice optimization algorithm (RIME) is employed to adaptively optimize mode number and penalty factor of variational mode decomposition (VMD)

which is then used to decompose the original wind power series. Subsequently

the decomposed modes are then reconstructed using sample entropy

thereby reducing the input dimension and suppressing noise. In the forecasting stage

an iTransformer-based iFANformer model is developed. By replacing the conventional feed-forward network with Fourier analysis networks (FAN)

learnable frequency features are incorporated to strengthen the model's capability of capturing periodic characteristics in time series data. Finally

comparative experiment and ablation analysis are carried out based on real wind farm data. Results show that the proposed method outperforms baseline models in forecasting accuracy

validating the effectiveness of the adaptive decomposition and frequency-enhancement strategies.

关键词

Keywords

references

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兰考三农职业学院

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WANG Lingbai(1. College of Automation & Electrical Engineering, Lanzhou Jiaotong University

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