基于DGAT-Transformer组合算法的区域光伏出力数据质量增强与超短期功率预测方法

任惠; 于光发; 强涵悦; 王飞; 甄钊; 常喜强

doi:10.19912/j.0254-0096.tynxb.2024-2352

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基于DGAT-Transformer组合算法的区域光伏出力数据质量增强与超短期功率预测方法

更新时间：2026-07-21

- 基于DGAT-Transformer组合算法的区域光伏出力数据质量增强与超短期功率预测方法
- Vol. 47, Issue 5, Pages: 639-649(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2024-2352
  CLC： TM615
- Online First：04 June 2026，
  
  Published：2026
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任惠, 于光发, 强涵悦, et al. 基于DGAT-Transformer组合算法的区域光伏出力数据质量增强与超短期功率预测方法[J]. 2026, 47(5): 639-649.
DOI：

任惠, 于光发, 强涵悦, et al. 基于DGAT-Transformer组合算法的区域光伏出力数据质量增强与超短期功率预测方法[J]. 2026, 47(5): 639-649. DOI： 10.19912/j.0254-0096.tynxb.2024-2352.

摘要

针对区域内各电站的光伏数据存在不同程度的缺失值而导致预测精度低的问题

提出基于时空关联性与动态图注意力网络建模的光伏功率缺失数据填充和基于周期—趋势分量解耦模型的分布式光伏功率预测的新方法框架。首先基于贝叶斯算法优化后的变分模态分解模型对光伏功率分解为多个趋势平稳的子序列

并将对应子序列合并构造特征矩阵。其次

提出基于动态图注意力网络和Transformer组合模型

提高对特征矩阵在时空双维度上动态关联关系表征的准确性

并利用此模型对缺失时刻各个子序列预测后加以重构

得到光伏电站的完备数据集。最后

引入基于变分推断原理的周期—趋势分量解耦的预测模型（LaST）

将光伏功率分解为周期性分量与趋势性分量

分别预测后加以重构

以提高光伏预测精度。仿真结果验证了该文所提方法获得的数据集真实性更高

并以此作为训练样本的预测精度显著优于其他方法

同时验证了LaST模型相较于传统算法在光伏预测中的优越性能。

Abstract

To address the decline in prediction accuracy caused by varying degrees of missing photovoltaic data across regional power stations

this paper proposes a novel framework that integrates photovoltaic power data imputation based on spatiotemporal correlation and dynamic graph attention network modeling with distributed photovoltaic power prediction leveraging a period-trend component decoupling approach. Specifically

a variational mode decomposition （VMD） model optimized via Bayesian algorithms is first employed to decompose the photovoltaic power series into multiple trend-stationary subsequences

which are then merged to form a comprehensive feature matrix. Subsequently

a hybrid model combining a dynamic graph attention network and Transformer is developed to more accurately capture the dynamic correlations within the feature matrix across spatiotemporal dimensions. This model is utilized to predict and reconstruct the missing subsequences

resulting in a complete photovoltaic dataset. Finally

a prediction model based on the principle of variational inference

termed the period-trend component decoupling model （LaST）

is introduced to further decompose the photovoltaic power into periodic and trend components. These components are individually modeled and predicted before being reconstructed to enhance overall prediction accuracy. Simulation results validate that the proposed method generates datasets with higher fidelity

which

when used as training samples

significantly improve prediction performance. Moreover

the superiority of the LaST model over conventional approaches in photovoltaic power forecasting is also demonstrated.

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