AI 中文总结
研究电动卡车能耗预测，通过考虑能量损失模型将物理原理融入数据驱动方法，用贝叶斯线性回归等模型预测能耗并估计不确定性，复杂模型能提高预测准确性和可靠性。
AI 中文摘要
在本工作中，我们通过考虑一个能解释车辆运行期间不同能量损失源的模型，将第一原理物理纳入数据驱动方法的构建。结果表明，基于此物理感知模型的贝叶斯线性回归相比标准线性回归可提高预期能耗的可靠性。此外，基于相同物理模型的更复杂机器学习模型如神经网络和梯度提升回归树能进一步提高能量预测准确性并显著优于标准版本。除了能耗点预测，我们还开发了一个以预测标准差形式估计相应不确定性的框架，结果表明所有模型都能较好地学习估计不确定性。
英文摘要
In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression. Further, it is shown that more complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can further improve the accuracy in energy forecasting and significantly outperform standard versions of the same machine learning models. In addition to point predictions of the energy consumption, we develop a framework for estimating the corresponding uncertainty in the form of predicted standard deviation. Our results show that all of the models learn to estimate the uncertainty reasonably well.
Comments22 pages, 8 figures
