用动态超图捕捉风电场时空关联，提升超短期预测精度
STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
- 用超图建模多风电场高阶空间关系，优于传统图结构
- 在GEFCom数据集上优于现有方法，显著提升预测准确率
- 适合关注风电预测、时空建模的工程师与研究人员
利用风力发电场之间的时空相关性可显著提升超短期风电功率预测精度。然而，这些相关性的复杂性和动态性带来了建模挑战。为此，本文提出一种时空动态超图学习（STDHL）模型。该模型采用超图结构表示风力发电场间的空间特征，相较于仅能捕捉成对节点关系的传统图结构，超图通过超边连接多个节点，能够有效表征和传播高阶空间特征。STDHL引入新颖的动态超图卷积层以建模动态空间相关性，并采用分组时间卷积层实现通道无关的时间建模。模型通过时空编码器从多源协变量中提取特征，并通过预测解码器映射至分位数结果。在GEFCom数据集上的实验表明，该模型优于现有先进方法。进一步分析揭示了时空协变量在提升超短期预测精度中的关键作用。
原文摘要 · Abstract (English)
Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (STDHL) model. This model uses a hypergraph structure to represent spatial features among wind farms. Unlike traditional graph structures, which only capture pair-wise node features, hypergraphs create hyperedges connecting multiple nodes, enabling the representation and transmission of higher-order spatial features. The STDHL model incorporates a novel dynamic hypergraph convolutional layer to model dynamic spatial correlations and a grouped temporal convolutional layer for channel-independent temporal modeling. The model uses spatio-temporal encoders to extract features from multi-source covariates, which are mapped to quantile results through a forecast decoder. Experimental results using the GEFCom dataset show that the STDHL model outperforms existing state-of-the-art methods. Furthermore, an in-depth analysis highlights the critical role of spatio-temporal covariates in improving ultra-short-term forecasting accuracy.
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