基于贝叶斯优化超参数的光伏功率区间预测方法

陆佳恩; 赵健; 李小勇

doi:doi:10.19912/j.0254-0096.tynxb.2024-1938

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基于贝叶斯优化超参数的光伏功率区间预测方法

更新时间：2026-04-08

- 基于贝叶斯优化超参数的光伏功率区间预测方法
- 基于贝叶斯优化超参数的光伏功率区间预测方法
- 太阳能学报 2026年47卷第3期页码：644-655
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1938
  中图分类号： TM615
- 网络首发：2026-04-07，
  
  纸质出版：2026
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陆佳恩, 赵健, 李小勇. 基于贝叶斯优化超参数的光伏功率区间预测方法[J]. 太阳能学报, 2026,47(3):644-655.

陆佳恩, 赵健, 李小勇. 基于贝叶斯优化超参数的光伏功率区间预测方法[J]. 2026, 47(3): 644-655.
陆佳恩, 赵健, 李小勇. 基于贝叶斯优化超参数的光伏功率区间预测方法[J]. 太阳能学报, 2026,47(3):644-655. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1938.

陆佳恩, 赵健, 李小勇. 基于贝叶斯优化超参数的光伏功率区间预测方法[J]. 2026, 47(3): 644-655. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1938.

摘要

针对光伏发电中光伏功率存在的随机、间歇和波动问题对电网安全的挑战

提出一种创新的深度学习模型——基于融合空时特征提取的深度残差时序卷积-高斯过程回归DRSTCG-GPR模型。该模型融合空时特征

在残差模块引入软阈值与注意力机制

通过贝叶斯算法自动优化超参数

并利用高斯过程回归量化预测的不确定性

最终实现短期光伏功率的高精度区间预测。实验对比表明

相较于传统卷积神经网络（CNN）、门控循环单元（GRU）及卷积门控循环单元（CGRU）模型

该模型在点预测精度（R2提升2.20%）、区间预测覆盖性能（ICP提升2.10%）及概率预测可靠性上均展现出显著优势。

Abstract

To address the challenges to grid security posed by the randomness

intermittency

and fluctuations of photovoltaic （PV） power in PV power generation planning

an innovative deep learning model—the DRSTCG-GPR model based on fused spatiotemporal feature extraction—is proposed. This model fuses spatiotemporal features

introduces soft thresholding and attention mechanisms into the residual module

automatically optimizes hyperparameters via a Bayesian algorithm

and quantifies the uncertainty of predictions using Gaussian process regression

ultimately achieving high-precision interval prediction for short-term photovoltaic （PV） power.. Experimental comparisons show that

compared to traditional CNN

GRU

and CGRU models

this model exhibits significant advantages in point prediction accuracy （² improvement of 2.20%）

interval prediction coverage performance （ improvement of 2.10%）

and probabilistic prediction reliability.

关键词

Keywords

references

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