基于多头注意力机制的ResNet-UNet短期风电功率预测

顾婷婷; 黄亦露; 王亚男; 任晨平; 郑芊彤

doi:doi:10.19912/j.0254-0096.tynxb.2024-2351

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基于多头注意力机制的ResNet-UNet短期风电功率预测

更新时间：2026-04-08

- 基于多头注意力机制的ResNet-UNet短期风电功率预测
- Vol. 47, Issue 3, Pages: 474-480(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2351
  CLC： TM614
- Online First：07 April 2026，
  
  Published：2026
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顾婷婷, 黄亦露, 王亚男, et al. 基于多头注意力机制的ResNet-UNet短期风电功率预测[J]. 2026, 47(3): 474-480.
DOI：

顾婷婷, 黄亦露, 王亚男, et al. 基于多头注意力机制的ResNet-UNet短期风电功率预测[J]. 2026, 47(3): 474-480. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2351.

摘要

为深入挖掘精细化的数值天气预报信息与风电功率之间的关联特征

提出一种基于多头注意力机制的ResNet-UNet短期风电功率预测方法。首先

考虑不同高度层的风向、风速、气压、相对湿度等气象因子

以网格为单元对数值预报进行特征提取并形成高维特征场。然后

融合UNet模型和ResNet模型

引入多头注意力机制捕获数值预报空间格点的相关特性

搭建风电功率预测模型。最后

采用浙江省某风电场的真实数据进行验证

并与UNet、ResNet、LSTM、BP模型进行对比分析

结果表明

所提出的基于多头注意力机制的ResNet-UNet预测方法能够有效提高预测精度。

Abstract

Numerical weather prediction has an important impact on the accuracy of short-term wind power prediction models. In order to fully mine the deep mapping relationship between the information of numerical weather prediction and actual wind power

this paper proposes a short- term wind power forecasting model based on ResNet-UNet model with incorporation of multi-head attention mechanism. Firstly

considering the meteorological factors such as wind direction

wind speed

air pressure

temperature

relative humidity at different altitude levels

the features of numerical weather prediction information are extracted using the grid as a unit and then form the high-dimensional feature vector. Secondly

a wind power prediction model is constructed by fusing the UNet model and ResNet model

in which a multi-head attention mechanism is introduced to capture the spatial correlation characteristics of numerical weather prediction. Finally

the actual data of a wind farm in Zhejiang province is used to verify the model and compared with the prediction accuracy of the UNet

the ResNet

the LSTM

the BP models. The results indicate that the proposed method can effectively impove prediction accuracy.

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