Home » Publication » 27851

Dettaglio pubblicazione

2023, 2023 31st Mediterranean Conference on Control and Automation (MED) Proceedings, Pages 500-506

Load Demand Prediction for Electric Vehicles Smart Charging through Consensus-based Federated Learning (04b Atto di convegno in volume)

Menegatti D., Pietrabissa A., Manfredi S., Giuseppi A.

Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the power system balancing. In this direction, we propose a short-term load demand predictor, based on distributed Long Short-Term Memory Networks, that employs consensus and fully-decentralized Federated Learning (FL) algorithms to seek cooperation among multiple points of charge without the requirement of sharing any user-related data.
ISBN: 979-8-3503-1543-1
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma