Abstract: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.
| Comments: | 18 pages, 19 figures |
| Subjects: | Applications (stat.AP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.19841 [stat.AP] |
| (or arXiv:2604.19841v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2604.19841 arXiv-issued DOI via DataCite (pending registration) |
From: Kaoutar Bouaachra [view email]
[v1]
Tue, 21 Apr 2026 09:52:22 UTC (3,993 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。