Predicting hybrid vehicles' fuel and electric consumption using multitask learning

Predicting energy (fuel and electric) consumption of hybrid vehicles is important on different levels: vehicle industry as a whole, individuals, and can also pave the way towards a more sustainable future. Despite its importance, providing accurate predictions is quite a challenging task. Many essential factors impacting energy consumption, including travel time, average speed, etc., needless to say, these features are not available beforehand. However, these factors are available in our data-set. To use these factors effectively, in this paper, we propose including them as different tasks in a multitask setting to help our main problem of energy consumption. The promise of this approach is that since these tasks are relevant, learning them together would provide a common feature space sharing information about all tasks. More importantly, this shared feature space would carry important information helping energy consumption in particular. In multitask learning, two important issues are task dominance and conflicting gradients of different tasks. Different studies have addressed these two separately. In this paper, we propose a method tackling these two problems simultaneously. We show experimentally the success of this method in comparison to state-of-the-art.