Spatio-temporal modelling of electric vehicle charging demand

2026-04-21

AI Summary

This study addresses the limitations of existing electric vehicle (EV) charging demand forecasting, which often relies on outdated data and fails to capture the scale and behavioral heterogeneity of modern charging networks. The authors construct a large-scale longitudinal dataset of EV charging activity in Scotland spanning 2022–2025 and introduce the first unified probabilistic framework that models charging demand as a spatiotemporal latent Gaussian field, integrating spatial dependencies, temporal dynamics, and covariate effects. Leveraging integrated nested Laplace approximation (INLA) for efficient Bayesian inference, the proposed approach achieves site-level prediction accuracy comparable to state-of-the-art machine learning models while providing reliable uncertainty quantification and interpretable decomposition of spatiotemporal components. This enables risk-aware decision support for power grid dispatch and infrastructure planning. The publicly released dataset establishes a new benchmark for the field.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• electric vehicle charging demand
• spatio-temporal forecasting
• charging infrastructure planning
• behavioral diversity
• grid management

Innovation

• spatio-temporal modelling
• latent Gaussian field
• Integrated Nested Laplace Approximation
• uncertainty quantification
• electric vehicle charging demand