Spatio-temporal modelling of electric vehicle charging demand
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 captur...
| Authors | Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude et al. |
|---|---|
| Primary category | stat.AP |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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