  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 paper an ensemble learning approach based on deep neural
networks (ENN) that is designed to reduce the predictive uncertainty and to
output measures of such uncertainty. We evaluated it using the publicly
available Vehicle Energy Dataset (VED) and compared it with several baselines
per vehicle and energy type. The results showed a high predictive performance
and they allowed to output a measure of predictive uncertainty.
