基于时空双流网络与多重注意力的光伏功率预测

李恒杰; 隆贤华; 周云; 冯冬涵; 马喜平

doi:10.19912/j.0254-0096.tynxb.2024-1741

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 51-59. DOI: 10.19912/j.0254-0096.tynxb.2024-1741

基于时空双流网络与多重注意力的光伏功率预测

李恒杰¹, 隆贤华¹, 周云², 冯冬涵², 马喜平³

作者信息 +

PHOTOVOLTAIC POWER PREDICTION BASED ON SPATIO-TEMPORAL DUAL-STREAM NETWORK AND MULTI-ATTENTION

Li Hengjie¹, Long Xianhua¹, Zhou Yun², Feng Donghan², Ma Xiping³

Author information +

文章历史 +

摘要

针对光伏发电的间歇性和随机性给光伏功率预测带来的准确性不足问题,提出基于时空双流网络与多重注意力的短期光伏功率预测模型。模型结合ModernTCN、ITransformer、GRU及TimesNet网络,充分挖掘光伏发电的空间分布特性和时间序列动态性,利用迭代交叉注意力机制有效融合独立提取的特征,形成包含丰富时空信息的特征向量,从而有效提升光伏功率的短期预测精度。实验结果证实,该模型在预测精度方面要优于现有主流时序模型。

Abstract

For accuracy limitations in photovoltaic （PV） power prediction due to the intermittent and stochastic nature of PV power generation, this study proposes a short-term PV power prediction model based on spatio-temporal dual-stream networks and multi-attention. The model integrates networks ModernTCN, ITransformer, GRU, and TimesNet networks, effectively exploring the spatial distribution characteristics and temporal dynamics of PV power generation. In addition, the model fuses extracted features, and generates feature vectors rich in Spatio-Temporal information by an iterative cross-attention feature mechanism. Experimental results demonstrate that the model is superior to the current mainstream timing series models in terms of prediction accuracy.

导出引用

李恒杰, 隆贤华, 周云, 冯冬涵, 马喜平. 基于时空双流网络与多重注意力的光伏功率预测[J]. 太阳能学报. 2026, 47(2): 51-59 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1741

Li Hengjie, Long Xianhua, Zhou Yun, Feng Donghan, Ma Xiping. PHOTOVOLTAIC POWER PREDICTION BASED ON SPATIO-TEMPORAL DUAL-STREAM NETWORK AND MULTI-ATTENTION[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 51-59 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1741

中图分类号： TM615

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