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

Thorgeirsson, Adam Thor

出版	KIT Scientific Publishing, 2024-09-03
ISBN	37315137149783731513711
URL	http://books.google.com.hk/books?id=02gfEQAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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.