Work

Federated Learning with Mixed Encryption for Distributed Energy Resources: Forecasting Electricity Charging Demand at Multiple Electrical Vehicle Charging Stations

Year: 2025

Type: conference-paper

Abstract: Sharing information or data between actors is crucial for forecasting distributed energy resources. However, collaboration involving the exchange of confidential data requires privacy-preserving models. Current models rely solely on fully encrypted frameworks, neglecting publicly available data or enforcing encryption on both private and public data. Furthermore, it is unknown whether a combination of public and private data types is beneficial to federated learning on spatiotemporal energy applications, in particular forecasting. In this work, we introduce a mixed encryption setting incorporating public data (e.g. weather, calendar) and private data relative to energy measurements. In this case study of day-ahead electric consumption of neighboring Electric Vehicle charging stations, we show that combining data increases the accuracy by up to 6% in RMSE and that mixed encryption reduces the computation time by up to 2 times compared to full encryption.

DOI: 10.1109/powertech59965.2025.11180597

Authors: Lukas Stippel, Simon Camal, Georges Kariniotakis

Institutions: Université Paris Sciences et Lettres, Centre Procédés, Energies Renouvelables et Systèmes Energétiques, École Nationale Supérieure des Mines de Paris

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4414956961

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