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Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks. (arXiv:2304.07073v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
The transportation sector accounts for about 25% of global greenhouse gas
emissions. Therefore, an improvement of energy efficiency in the traffic sector
is crucial to reducing the carbon footprint. Efficiency is typically measured
in terms of energy use per traveled distance, e.g. liters of fuel per
kilometer. Leading factors that impact the energy efficiency are the type of
vehicle, environment, driver behavior, and weather conditions. These varying
factors introduce uncertainty in estimating the vehicles' energy efficiency. We
propose in this …
arxiv behavior carbon carbon footprint driver efficiency emissions energy energy efficiency ensemble environment global impact improvement networks neural networks paper per prediction sector terms traffic transportation type uncertainty weather