一种多视角多时间尺度超图赋能的时空框架用于电动汽车充电预测
A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting
AI总结：
HyperCast通过多视角多时间尺度超图框架，有效建模电动汽车充电的高阶时空依赖性，提升预测准确性。
摘要：
准确的电动汽车（EV）充电需求预测对于稳定电网运行和主动参与电力市场至关重要。现有预测方法，特别是基于图神经网络的方法，往往局限于建模站点之间的 pairwise 关系，无法捕捉城市充电网络中复杂的、群体性的动态。为解决这一差距，我们开发了一种新的预测框架，称为HyperCast，利用超图的表达能力来建模隐藏在电动汽车充电模式中的更高阶时空依赖性。HyperCast整合了多视角超图，这些超图捕捉了静态的地理接近性和动态需求基础上的功能相似性，并结合多时间尺度输入以区分近期趋势和周周期性。该框架采用专门的超时空块和定制的交叉注意力机制，以有效融合这些多样来源的信息：视角和时间尺度。在四个公开数据集上的广泛实验表明，HyperCast显著优于一系列最先进的基线方法，证明了显式建模集体充电行为对于更准确预测的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
