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.
Load Demand Prediction for Electric Vehicles Smart Charging through Consensus-based Federated Learning / Menegatti, D.; Pietrabissa, A.; Manfredi, S.; Giuseppi, A.. - (2023), pp. 500-506. (Intervento presentato al convegno 31st Mediterranean Conference on Control and Automation, MED 2023 tenutosi a cyp nel 2023) [10.1109/MED59994.2023.10185743].
Load Demand Prediction for Electric Vehicles Smart Charging through Consensus-based Federated Learning
Manfredi S.;
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
