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[2602.20782] On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Summary
This paper explores electric vehicle energy demand forecasting using federated learning, comparing various forecasting methodologies to enhance accuracy and efficiency.
Why It Matters
As electric vehicles proliferate, accurate energy demand forecasting is crucial for optimizing resource allocation and ensuring grid reliability. This study addresses the challenges posed by fragmented data and privacy concerns, highlighting the potential of federated learning in improving forecasting accuracy while preserving user privacy.
Key Takeaways
- Electric vehicle energy demand forecasting is essential for efficient energy management.
- Federated learning can address privacy concerns while improving forecasting accuracy.
- Gradient boosted trees (XGBoost) outperform traditional models in prediction accuracy and energy efficiency.
Computer Science > Machine Learning arXiv:2602.20782 (cs) [Submitted on 24 Feb 2026] Title:On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning Authors:Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis, Yannis Theodoridis View a PDF of the paper titled On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning, by Andreas Tritsarolis and 3 other authors View PDF HTML (experimental) 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 s...
