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

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 probab...

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Autor principal:	Thorgeirsson, Adam Thor
Format:	Online
Idioma:	anglès
Publicat:	KIT Scientific Publishing 2024
Matèries:	Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials
Accés en línia:	https://library.oapen.org/handle/20.500.12657/93282
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