构建苏格兰电动车充电需求数据集，用概率模型精准预测时空变化。
Spatio-temporal modelling of electric vehicle charging demand
- 将充电需求建模为时空潜在高斯场，统一处理空间依赖与时间动态。
- 在站点级预测中精度媲美机器学习模型，且提供可解释的分解结果。
- 适合电网规划与风险评估，开源数据助力社区研究。
准确预测电动汽车（EV）充电需求对电网管理与基础设施规划至关重要。然而，当前研究仍依赖老旧基准数据集（如Palo Alto, 2020），难以反映现代充电网络的规模与行为多样性。为此，我们引入一个覆盖苏格兰（2022–2025）的大规模纵向数据集，并公开作为社区基准。基于该数据，我们将充电需求建模为时空潜在高斯场，采用集成嵌套拉普拉斯近似（INLA）进行近似贝叶斯推断。模型在统一的概率框架内联合捕捉空间相关性、时间动态与协变量影响。在站点级预测任务中，方法表现优于或媲美主流机器学习基线，同时提供严谨的不确定性量化及可解释的空间-时间分解，对风险敏感型基础设施规划具有重要意义。
原文摘要 · Abstract (English)
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; such as the Palo Alto (2020) dataset; that fail to reflect the scale and behavioral diversity of modern charging networks. To address this, we introduce a novel large-scale longitudinal dataset collected across Scotland (2022 2025), which release it as an open benchmark for the community. Building on this dataset, we formulate EV charging demand as a spatio-temporal latent Gaussian field and perform approximate Bayesian inference via Integrated Nested Laplace Approximation (INLA). The resulting model jointly captures spatial dependence, temporal dynamics, and covariate effects within a unified proba bilistic framework. On station-level forecasting tasks, our approach achieves competitive predictive accuracy against machine learning baselines, while additionally providing principled uncertainty quan tification and interpretable spatial and temporal decompositions properties that are essential for risk-aware infrastructure planning.
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