Short-term Wind Power Prediction Based on Improved Informer Incorporating Spatiotemporal Features

GUO Lijin; CAI Haoxiang; SUN Miao

doi:10.19805/j.cnki.jcspe.2026.250018

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Short-term Wind Power Prediction Based on Improved Informer Incorporating Spatiotemporal Features

更新时间：2026-10-10

- Short-term Wind Power Prediction Based on Improved Informer Incorporating Spatiotemporal Features
- Chinese Journal of Power Engineering Vol. 46, Issue 4, Pages: 101-109(2026)
- 作者机构：
  
  1. 天津工业大学控制科学与工程学院,天津,300387
  2. 天津市电气装备智能控制重点实验室,天津,300387
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250018
  CLC： TM614
- Published：2026
- 稿件说明：
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郭利进, 蔡昊翔, 孙淼. 融合时空特性的改进Informer风电功率短期预测[J]. 动力工程学报, 2026, 46(4): 101-109.
郭利进, 蔡昊翔, 孙淼. 融合时空特性的改进Informer风电功率短期预测[J]. 动力工程学报, 2026, 46(4): 101-109. DOI： 10.19805/j.cnki.jcspe.2026.250018.

DOI： 10.19805/j.cnki.jcspe.2026.250018

摘要

为克服风电功率序列的不稳定性导致预测精度低以及传统预测方法在捕捉风电场间时空相关性方面的局限性

提出一种新型风电功率预测模型。首先采用改进的嵌入方式

提出时间-特征混合多层感知机(MLP)

以强化特征维度间多重相关性的捕获能力

提取全局时序与动态特征的关联信息。然后

提出空间特征交互机制(SFI)

利用双通道输入能力构建基于注意力机制的特征提取模块

获取特定风电场与周边风电场之间的动态空间依赖关系。最后

将融合多电场的时空特征图输入到编码器

得到预测结果。通过多组实际数据对所提出的方法进行了实验分析与验证。结果表明:所提出的预测方法在预测精度方面优于其他预测方法。

Abstract

To address the low prediction accuracy caused by the instability of wind power series and the limitations of traditional prediction methods in capturing the spatiotemporal correlation between wind farms

a novel wind power prediction model was proposed. Firstly

by employing an improved embedding method

a temporal-feature hybrid multilayer perceptron (MLP) was proposed to enhance the ability to capture multiple correlations among feature dimensions and extract correlation information between global temporal and dynamic features. Then

the spatial feature interaction (SFI) mechanism was proposed. Utilizing dual-channel input capability

a feature extraction module based on attention mechanism was constructed to acquire dynamic spatial dependencies between the target wind farm and surrounding wind farms. Finally

the spatiotemporal feature map incorporating multiple wind farms was input into the encoder to obtain the prediction results. The proposed method was experimentally analyzed and verified by multiple sets of actual data. Results show that the proposed prediction method outperforms other prediction methods in terms of prediction accuracy.

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references

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