计及相似日和MB-T2V-KSA Net的超短期光伏功率预测

李玉洪; 毕贵红; 杨楠; 王小玲; 陈世语

doi:10.19912/j.0254-0096.tynxb.2025-0342

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 439-456. DOI: 10.19912/j.0254-0096.tynxb.2025-0342

计及相似日和MB-T2V-KSA Net的超短期光伏功率预测

李玉洪, 毕贵红, 杨楠, 王小玲, 陈世语

作者信息 +

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING CONSIDERING SIMILAR DAYS AND MB-T2V-KSA NETWORK

Li Yuhong, Bi Guihong, Yang Nan, Wang Xiaoling, Chen Shiyu

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文章历史 +

摘要

针对现有光伏发电功率预测中存在的历史数据冗余、连续时间序列高度相关、训练样本量大、模型收敛时间长、光伏功率序列表现出显著随机性和非线性等问题,提出一种基于改进灰色关联分析、双模态混合分解与多分支深度学习模型的光伏功率预测方法。首先,通过相关性分析筛选出与光伏发电功率呈正相关的气象因素变量,并将其正相关系数作为权重引入灰色关联度分析方法中,结合对数标准化策略,提升相似日样本选取的准确性和判别力;然后,采用经验小波变换（EWT）和群分解（SWD）对光伏功率序列进行双模态混合分解,实现多尺度特征的规律互补,获得更为完备的输入特征量。在模型构建方面,设计多分支并行输入的深度学习预测模型MB-T2V-KSA Net,融合时间向量嵌入（Time2Vec）提取线性与周期性时序特征、双向门控循环单元（BiGRU）捕获上下文依赖信息、KAN实现高阶特征交互、自注意力机制（SA）对多源特征进行自适应加权融合,从而有效提升模型的时序建模能力与特征集成效果。实验结果表明,所提方法在不区分天气类型和区分多种典型天气条件下均表现出良好的预测精度和稳定性,特别是在恶劣天气条件下表现出优异的预测性能。

Abstract

To address issues in existing photovoltaic power forecasting, such as redundant historical data, strong correlations in consecutive time series, large training datasets, slow model convergence, and the significant randomness and nonlinearity of power outputs, this paper proposes a novel forecasting approach based on improved grey relational analysis, dual-modal hybrid decomposition, and a multi-branch deep learning model. First, meteorological variables positively correlated with PV power output are selected through correlation analysis. Their positive correlation coefficients are incorporated as weights into an enhanced grey relational analysis framework, combined with a logarithmic normalization strategy to improve the accuracy and discriminative capability of similar-day sample selection. Next, a dual-modal hybrid decomposition is performed on the PV power time series using empirical wavelet transform （EWT） and swarm decomposition （SWD）, enabling multi-scale feature complementarity and generating more comprehensive input features. For model construction, a multi-branch input-parallel deep learning architecture, MB-T2V-KSA Net （Multi-Branch input-parallel Time2Vec-BiGRU-KAN-SA）, is developed. This model integrates Time2Vec encoding to capture both linear and periodic temporal features, employs a Bidirectional Gated Recurrent Unit （BiGRU） to learn contextual dependencies, utilizes the Kolmogorov-Arnold Network （KAN） for high-order feature interaction, and applies a Self-Attention （SA） mechanism to adaptively fuse multi-source features. Together, these components significantly enhance the model’s ability in temporal modeling and feature integration.Experimental results demonstrate that the proposed method achieves high prediction accuracy and robustness regardles of weather type classification and diverse typical weather conditions, with particularly strong performance in adverse weather scenarios.

导出引用

李玉洪, 毕贵红, 杨楠, 王小玲, 陈世语. 计及相似日和MB-T2V-KSA Net的超短期光伏功率预测[J]. 太阳能学报. 2026, 47(7): 439-456 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0342

Li Yuhong, Bi Guihong, Yang Nan, Wang Xiaoling, Chen Shiyu. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING CONSIDERING SIMILAR DAYS AND MB-T2V-KSA NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 439-456 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0342

中图分类号： TM615

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