On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning

TL;DR

This paper compares various time series forecasting methods for electric vehicle energy demand, highlighting federated learning as a promising approach for balancing accuracy, privacy, and energy efficiency in decentralized settings.

Contribution

It provides a comprehensive performance comparison of statistical, machine learning, and deep learning models for EV energy demand forecasting, emphasizing federated learning benefits.

Findings

XGBoost outperforms other models in accuracy and energy efficiency.

Federated learning models offer a good balance between privacy and forecasting performance.

Deep neural networks like GRU and LSTM are less energy-efficient but still competitive.

Abstract

The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability, since it enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability. However, accurate EDF is a challenging problem due to external factors, such as the varying user routines, weather conditions, driving behaviors, unknown state of charge, etc. Furthermore, as concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented, resulting in distributed datasets scattered across different data silos and/or edge devices, calling for federated learning solutions. In this paper, we investigate different well-established time series forecasting methodologies to address the EDF problem, from statistical methods (the ARIMA family) to traditional machine learning models (such as XGBoost) and deep neural networks (GRU and LSTM). We provide an overview of these methods through a performance comparison over four real-world EVSE datasets, evaluated under both centralized and federated learning paradigms, focusing on the trade-offs between forecasting fidelity, privacy preservation, and energy overheads. Our experimental results demonstrate, on the one hand, the superiority of gradient boosted trees (XGBoost) over statistical and NN-based models in both prediction accuracy and energy efficiency and, on the other hand, an insight that Federated Learning-enabled models balance these factors, offering a promising direction for decentralized energy demand forecasting.