电动重型商用车辅助能耗预测
Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
AI总结：
针对电动重型商用车能耗预测中因异质性子群体导致的辛普森悖论问题，本文提出在数据子集上训练多个回归模型，以提升回归性能并获得更一致可靠的 LIME 解释。
摘要：
准确的能耗预测对于优化电动商用重型车辆的运营至关重要，例如充电的路线规划。此外，理解为何做出某些预测对于此类预测模型获得用户信任并在实际中部署至关重要。由于商用车辆在运输任务、环境和驾驶员方面存在差异，因此在构建用于预测能耗的人工智能系统时，预期会存在异质性群体。输入特征与目标值之间的依赖关系在不同子群体之间预计也会有所不同。这种统计现象的一个众所周知的例子是辛普森悖论。在本文中，我们说明了这种设置对现有的可解释人工智能（XAI）方法（例如 LIME 或 SHAP）构成了挑战，这些方法产生全局特征统计量，导致它们产生误导性的结果。我们展示了一种潜在的解决方案，即在数据子集上训练多个回归模型。这不仅带来了更优的回归性能，而且还产生了更相关且一致的 LIME 解释。鉴于所采用的分组对应于相关的子群体，输入特征与目标值之间的关联在每个簇内是一致的，但在不同簇之间是不同的。在合成数据集和真实世界数据集上的实验表明，将复杂问题拆分为更简单的问题可以产生更好的回归性能和可解释性。
英文摘要：
Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for 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 AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is the Simpson paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g. LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
