基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法

师重阳; 曹腾飞; 李坤昌; 陈来军

doi:10.19912/j.0254-0096.tynxb.2025-0689

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法

更新时间：2026-10-15

- 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法
- 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法
- 太阳能学报 2026年47卷第9期页码：219-228
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金（62461052）;青海省“揭榜挂帅”重大科技专项（2024-GX-A3)
- DOI：10.19912/j.0254-0096.tynxb.2025-0689
  中图分类号： TM615
- 网络首发：2026-10-10，
  
  纸质出版：2026
- 稿件说明：
移动端阅览
师重阳, 曹腾飞, 李坤昌, 等. 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法[J]. 太阳能学报, 2026,47(9):219-228.

师重阳, 曹腾飞, 李坤昌, et al. 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法[J]. 2026, 47(9): 219-228.
师重阳, 曹腾飞, 李坤昌, 等. 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法[J]. 太阳能学报, 2026,47(9):219-228. DOI： 10.19912/j.0254-0096.tynxb.2025-0689.

师重阳, 曹腾飞, 李坤昌, et al. 基于Transformer-Mamba混合架构的天空图像与光伏功率协同预测方法[J]. 2026, 47(9): 219-228. DOI： 10.19912/j.0254-0096.tynxb.2025-0689.

摘要

为应对云层动态变化复杂、光伏功率与环境因素关系高度非线性的建模难题

提出一种融合 Transformer 与 Mamba 的多模态深度学习框架

通过联合建模天空图像的时空特征与红外光谱特征

实现更高精度的光伏功率预测。该研究采用Transformer建模云层运动的时序动态

利用Mamba提取红外光谱信息

并通过跨模态特征融合建立天空图像与发电功率之间的非线性关联。实验基于斯坦福天空图像与光伏发电数据集

验证了该模型在天空图像预测和光伏发电预测两方面的性能。消融实验结果表明

结合红外光谱、时序建模和多尺度特征提取的完整模型在图像预测任务中取得最优均方根误差（13.04 kW）和结构衡量指标（0.94653）

同时在光伏功率预测任务中相较现有方法误差降低4.5%。在典型多云天气场景下

模型能较好地跟踪云层遮挡引起的光伏功率波动趋势。

Abstract

Accurately forecasting sky imagery and its impact on solar power generation is essential for intelligent grid scheduling and the efficient management of renewable energy resources. However

capturing the dynamic evolution of cloud patterns and their complex

nonlinear relationship with photovoltaic （PV） power output remains a formidable challenge. To tackle this problem

we propose a multimodal deep learning framework that integrates Transformer and Mamba architectures

effectively leveraging both spatiotemporal features from sky images and infrared spectral information to improve forecasting precision. Within this framework

the Transformer is responsible for modeling the temporal dynamics of cloud motion

while Mamba focuses on extracting salient features from infrared spectra. A cross-modal feature fusion strategy is employed to learn the intricate nonlinear mapping between sky imagery and power output. We evaluate our model on the Stanford sky image and PV generation datasets

conducting extensive experiments that demonstrate superior performance in both sky image forecasting and PV power prediction tasks. Ablation studies reveal that the full model—incorporating infrared data

temporal modeling

and multi-scale feature extraction—achieves an RMSE of 13.04kW and an SSIM of 0.94653 for image prediction. Moreover

the proposed method achieves an MAE of 1.50 kW

approximately 4.5% lower than that of the second-best Tau model.Under typical cloudy weather conditions

the model is able to effectively track the trend of photovoltaic power fluctuations caused by cloud occlusion.

关键词

Keywords

references

周勃, 李二超. 一种考虑风光不确定性的热电混合共享储能双层优化配置方法[J]. 太阳能学报, 2025, 46(3): 189-198.

Zhong Y J, Wu Y K.Short-term solar power forecasts considering various weather variables[C]//2020 International Symposium on Computer, Consumer and Control (IS3C). Taichung City, Taiwan, China, 2020: 432-435.

刘兴冉, 张宏晔, 闫海明, 等. 区域复杂地形晴日太阳辐射估算研究[J]. 太阳能学报, 2022, 43(8): 174-180. Liu X R, Zhang H Y, Yan H M, et al. Estimation of regional solar radiation on clear days over complex terrain[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 174-180.

徐姝影, 张哲, 袁佳伟, 等. 考虑光伏跟踪装置的混合可再生能源系统优化与评价[J]. 太阳能学报, 2025, 46(3): 123-131. Xu S Y, Zhang Z, Yuan J W, et al. Optimization and evaluation of hybrid renewable energy systems considering PV tracking devices[J]. Acta Energiae Solaris Sinica, 2025, 46(3): 123-131.

Vaswani A, Shazeer N, Parmar N, et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.

Jing T, Chen S L, Navarro-Alarcon D, et al.SolarFusionNet: enhanced solar irradiance forecasting via automated multi-modal feature selection and cross-modal fusion[J]. IEEE Transactions on Sustainable Energy, 2025, 16(2): 761-773.

Wei X S, Yue D, Hancke G P, et al.Ultra short-term solar irradiance forecast based on multimodal data fusion and fuzzification[J]. IEEE Transactions on Industrial Informatics, 2025, 21(4): 3256-3265.

Zhao X H, Fan H Y, Guo S, et al.Sky image-informed hybrid model for ultra-short-term prediction of photovoltaic power output[C]//4th Energy Conversion and Economics Annual Forum (ECE Forum 2024). Beijing, China, 2025: 375-379.

Meddahi A, Tuomiranta A, Guillon S.Skill-driven data sampling and deep learning framework for minute-scale solar forecasting with sky images[J]. Solar RRL, 2025, 9(4): 2400664.

王东风, 李青博, 张博洋, 等. 考虑季节性与趋势特征的光伏功率预测模型研究[J]. 太阳能学报, 2025, 46(3): 348-356. Wang D F, Li Q B, Zhang B Y, et al. Research on photovoltaic power prediction model considering seasonality and trend characteristics[J]. Acta Energiae Solaris Sinica, 2025, 46(3): 348-356.

Li H, Ma G, Wang B, et al.Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting[J]. Renewable Energy, 2025, 240: 122192.

Hategan S M, Stefu N, Petreus D, et al.Short-term forecasting of PV power based on aggregated machine learning and sky imagery approaches[J]. Energy, 2025, 316: 134595.

黄政之,厉小润,张天文. 基于可见光与红外图像的光伏阵列组串热斑检测方法[J]. 太阳能学报, 2025, 46(3): 454-460.

李宏扬, 高丙朋. 基于改进VMD和SNS-Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2023, 44(8): 292-300.

Nie Y H, Li X T, Scott A, et al.SKIPP’D: a SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting[J]. Solar Energy, 2023, 255: 171-179.

Tan C, Gao Z Y, Wu L R, et al.Temporal attention unit: towards efficient spatiotemporal predictive learning[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada, 2023: 18770-18782.

Yu G Z, Lu L, Tang B, et al.Ultra-short-term wind power subsection forecasting method based on extreme weather[J]. IEEE Transactions on Power Systems, 2023, 38(6): 5045-5056.

Yao T C, Wang J, Wu H Y, et al.A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with Python toolkit[J]. Solar Energy, 2021, 230: 122-130.

Li Y Y, Song L D, Zhang S, et al.A TCN-based hybrid forecasting framework for hours-ahead utility-scale PV forecasting[J]. IEEE Transactions on Smart Grid, 2023, 14(5): 4073-4085.

LI Q Q, XU Y, CHEW B S H, et al. An integrated missing-data tolerant model for probabilistic PV power generation forecasting[J]. IEEE Transactions on Power Systems, 2022, 37(6): 4447-4459.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

考虑光伏发电集群的系统电-碳耦合运营决策研究框架及展望

一种高增益软开关准Z源DC-DC变换器

双层柔性光伏支架结构风振系数风洞试验及数值仿真研究

考虑光伏出力的储能系统容量优化配置研究

基于聚类和混合提取的BiGRU-MDSA光伏功率短期预测