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.

准确的能源消耗预测对于优化电动商用重型车辆的运营至关重要，本文介绍了一种基于子集训练的回归模型，该模型在复杂问题中将其拆分为更简单的子问题，以实现更好的回归性能和解释性。

电动重型车辆辅助能源消耗预测

Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty  Vehicles

Creating an appropriate energy consumption prediction model is becoming an
important topic for drone-related research in the literature. However, a
general consensus on the energy consumption model is yet to be reached at
present. As a result, there are many variations that attempt to create models
that range in complexity with a focus on different aspects. In this paper, we
benchmark the five most popular energy consumption models for drones derived
from their physical behaviours and point to the difficulties in matching with a
realistic energy dataset collected from a delivery drone in flight under
different testing conditions. Moreover, we propose a novel data-driven energy
model using the Long Short-Term Memory (LSTM) based deep learning architecture
and the accuracy is compared based on the dataset. Our experimental results
have shown that the LSTM based approach can easily outperform other
mathematical models for the dataset under study. Finally, sensitivity analysis
has been carried out in order to interpret the model.

本文针对基于物理行为的五个能耗模型进行了基准测试，指出了与实际数据集的匹配困难，提出了一种基于 LSTM 的深度学习能量模型，并通过实验证明了该方法对于所研究的数据集可以轻松超越其他数学模型。最后，进行了敏感度分析以解释该模型。