用超图建模充电站集体行为，提升电动车充电预测精度
A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting
- 用超图捕捉多个充电站间的群体关系，超越传统成对连接
- 融合多视角与多时序数据，区分短期趋势与周周期规律
- 在4个公开数据集上显著优于现有方法，适合电网调度参考
精准的电动汽车（EV）充电需求预测对于电网稳定运行和电动车参与电力市场至关重要。现有基于图神经网络的方法通常仅能建模站点间的成对关系，难以捕捉城市充电网络中固有的复杂群体动态。为此，我们提出名为HyperCast的新框架，利用超图的强大表达能力，建模隐藏在电动车充电模式中的高阶时空依赖关系。HyperCast融合多视角超图，同时捕捉静态地理邻近性和动态需求相似性，并引入多时序输入以区分近期趋势与周周期规律。框架采用专用的超时空模块和定制交叉注意力机制，有效融合来自不同视角和时序的信息。在四个公开数据集上的大量实验表明，HyperCast显著优于多种先进基线方法，验证了显式建模集体充电行为对提升预测准确性的有效性。
原文摘要 · Abstract (English)
Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inherent in urban charging networks. To address this gap, we develop a novel forecasting framework namely HyperCast, leveraging the expressive power of hypergraphs to model the higher-order spatiotemporal dependencies hidden in EV charging patterns. HyperCast integrates multi-view hypergraphs, which capture both static geographical proximity and dynamic demand-based functional similarities, along with multi-timescale inputs to differentiate between recent trends and weekly periodicities. The framework employs specialized hyper-spatiotemporal blocks and tailored cross-attention mechanisms to effectively fuse information from these diverse sources: views and timescales. Extensive experiments on four public datasets demonstrate that HyperCast significantly outperforms a wide array of state-of-the-art baselines, demonstrating the effectiveness of explicitly modeling collective charging behaviors for more accurate forecasting.
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