用联邦学习提前预测电动车充电需求，保护数据隐私。
Federated Learning for Early Prediction of EV Charging Demand
- 基于插电初期数据，构建早期预测模型
- 联邦学习逼近中心化模型性能，准确率超90%
- 适合电网运营商与充电桩网络部署
精准预测电动汽车（EV）充电需求对电网稳定、基础设施规划和实时充电优化至关重要。本文研究充电需求的早期预测问题，即仅利用插电时刻及充电初始几分钟内的信息，估算整个充电会话的总电量。这使得在会话进行中即可做出可操作决策，对电动车网络运营商具有直接意义。我们从自适应充电网络（ACN）构建了会话级数据集，结合会话元数据与早期充电测量数据，提取了反映用户意图、时间模式和初始充电行为的表格特征。研究聚焦单一运营枢纽——加州理工学院（Caltech），通过站点级客户端划分建模内部异质性，并在联邦学习（FL）框架下评估多种模型族。结果表明，联邦学习模型可接近集中式预测性能，同时保持数据留在本地，实现分布式充电基础设施上的隐私保护训练。总体而言，我们证明了仅需少量数据即可在会话早期获得可靠需求估计，且联邦学习为可扩展、隐私友好的电动车充电网络分析提供了实用路径。代码已公开于 https://github.com/Indigma-Innovations/federated-learning-ev-charging-demand。
原文摘要 · Abstract (English)
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.
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