Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks
This work addresses uncertainty in energy efficiency prediction for the transportation sector, which is incremental as it applies an ensemble method to a known bottleneck.
The paper tackled the problem of predicting vehicle energy efficiency with uncertainty by proposing an ensemble of neural networks (ENN), achieving high predictive performance and providing uncertainty measures on the Vehicle Energy Dataset (VED).
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.