Federated Learning for Early Prediction of EV Charging Demand

TL;DR

This paper demonstrates that federated learning can accurately predict EV charging demand early in a session using minimal data, while preserving user privacy across distributed charging stations.

Contribution

It introduces a federated learning approach for early EV charging demand prediction that maintains data privacy and achieves performance close to centralized models.

Findings

Federated models nearly match centralized model accuracy.

Early prediction is feasible with minimal charging session data.

Federated learning enables privacy-preserving, scalable EV demand forecasting.

Abstract

Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimization. In this work, we study the problem of early prediction of charging demand, where the total energy of a session is estimated using only information available at plug-in time and during the first minutes of charging. This enables actionable decisions while the session is still in progress, which is of direct importance for EV network operators. We construct a session-level dataset from the Adaptive Charging Network (ACN), combining session metadata with early-window charging measurements, and derive tabular features capturing user intent, temporal patterns, and initial charging behavior. We focus on a single operational depot, Caltech, and model intra-depot heterogeneity through station-level client partitions while evaluating multiple model families in a federated learning (FL) setting. Our results show that federated models can approach centralized predictive performance while keeping data in-depot, enabling privacy-enhanced training across distributed charging infrastructures. Overall, we demonstrate that reliable demand estimates can be obtained early in the session with minimal data, and that FL provides a practical pathway toward scalable and privacy-aware analytics for EV charging networks. Code is available at https://github.com/Indigma-Innovations/federated-learning-ev-charging-demand.