Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning

Beschreibung

In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.

Autor*in

Adam Thor Thorgeirsson

Themen in »Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning«

Elektrofahrzeuge Reichweite Probabilistische Vorhersage Föderiertes Lernen Electric Vehicles Driving Range Probabilistic Predictions Federated Learning

Stimmen zu »Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning«

Details

Link teilen