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.

翻译：交通领域约占全球温室气体排放量的25%，因此提升交通部门的能效对于减少碳足迹至关重要。能效通常以单位行驶距离的能源消耗量（如每公里燃油升数）来衡量。影响能效的主要因素包括车辆类型、环境条件、驾驶员行为及天气状况。这些变化因素给车辆能效估计带来了不确定性。本文提出一种基于深度神经网络的集成学习方法（ENN），旨在降低预测不确定性并输出此类不确定性的度量指标。我们利用公开的车辆能源数据集（VED）进行评估，并与多种按车辆及能源类型划分的基线方法进行了比较。结果表明，该方法具有较高的预测性能，并能输出预测不确定性的度量值。