Spatio-temporal modelling of electric vehicle charging demand

TL;DR

This paper introduces a new large-scale Scottish EV charging dataset and a spatio-temporal Bayesian model that improves demand forecasting accuracy while providing uncertainty quantification for infrastructure planning.

Contribution

The paper presents a novel open benchmark dataset and a Bayesian spatio-temporal model for EV charging demand forecasting, capturing complex dependencies and uncertainties.

Findings

Model achieves competitive accuracy against machine learning baselines.

Provides principled uncertainty quantification for demand predictions.

Offers interpretable spatial and temporal decompositions.

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.