基于改进CNN-Autoformer网络的光伏功率短期概率预测方法

朱文志; 郭力; 刘一欣; 李彦榕; 李西良; 吴翠姑

doi:doi:10.19912/j.0254-0096.tynxb.2024-1968

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基于改进CNN-Autoformer网络的光伏功率短期概率预测方法

更新时间：2026-04-08

- 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法
- 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法
- 太阳能学报 2026年47卷第3期页码：678-689
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1968
  中图分类号： TM615
- 网络首发：2026-04-07，
  
  纸质出版：2026
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朱文志, 郭力, 刘一欣, 等. 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法[J]. 太阳能学报, 2026,47(3):678-689.

朱文志, 郭力, 刘一欣, et al. 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法[J]. 2026, 47(3): 678-689.
朱文志, 郭力, 刘一欣, 等. 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法[J]. 太阳能学报, 2026,47(3):678-689. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1968.

朱文志, 郭力, 刘一欣, et al. 基于改进CNN-Autoformer网络的光伏功率短期概率预测方法[J]. 2026, 47(3): 678-689. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1968.

摘要

针对光伏功率短期预测面临的不确定性难以准确刻画问题

提出一种基于改进CNN-Autoformer网络的光伏功率短期概率预测方法。首先

采用卷积神经网络提取并建立数值天气预报高维气象特征与光伏功率之间的映射关系;其次

利用自组织映射神经网络对天气类型进行削减归类

并作为光伏功率日序列的离散特征。在此基础上

构建时序Autoformer网络深度分解光伏功率时间序列

引入自相关机制捕捉光伏功率时间序列的周期性和趋势性特征;最后

结合极大似然估计与梯度优化

通过概率密度估计层输出得到光伏功率的概率分布参数。算例结果表明

相较于传统预测方法

所提方法在提高光伏功率概率预测性能方面具有明显优势。

Abstract

Addressing the challenge of accurately characterizing uncertainties in short-term photovoltaic （PV） power forecasting

this paper proposes a short-term PV probabilistic prediction method based on an improved CNN-Autoformer network. Firstly

convolutional neural network is used to extract and establish a mapping relationship between high-dimensional meteorological features and PV output based on numerical weather prediction. Secondly

a self-organizing map （SOM） neural network is employed to reduce and categorize weather types as discrete features of the daily PV sequence. Based on this

a temporal Autoformer network is constructed to deeply decompose the PV sequence

incorporating an autocorrelation mechanism to capture the periodicity and trend features. Finally

combining maximum likelihood estimation with gradient optimization

the parameters of the PV output probabilistic distribution are derived through a probability density estimation layer. Simulation results demonstrate that the proposed method can effectively improve the performance of PV probabilistic prediction compared to the comparative methods.

关键词

Keywords

references

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