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Accurate energy consumption prediction is crucial for optimizing theoperation of electric commercial heavy-duty vehicles, e.g., route planning forcharging. Moreover, understanding why certain predictions are cast is paramountfor such a predictive model to gain user trust and be deployed in practice.Since commercial vehicles operate differently as transportation tasks, ambient,and drivers vary, a heterogeneous population is expected when building an AIsystem for forecasting energy consumption. The dependencies between the inputfeatures and the target values are expected to also differ acrosssub-populations. One well-known example of such a statistical phenomenon is theSimpson paradox. In this paper, we illustrate that such a setting poses achallenge for existing XAI methods that produce global feature statistics, e.g.LIME or SHAP, causing them to yield misleading results. We demonstrate apotential solution by training multiple regression models on subsets of data.It not only leads to superior regression performance but also more relevant andconsistent LIME explanations. Given that the employed groupings correspond torelevant sub-populations, the associations between the input features and thetarget values are consistent within each cluster but different across clusters.Experiments on both synthetic and real-world datasets show that such splittingof a complex problem into simpler ones yields better regression performance andinterpretability.

Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles