融合物理机制的机器学习模型更准预测电动卡车能耗
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
- 用物理模型约束数据驱动方法，捕捉车辆能耗损失机制
- 贝叶斯线性回归误差降低，神经网络与梯度提升树更优
- 可同时输出能耗预测值和不确定性估计，适合实际部署
本文将第一性原理物理知识融入数据驱动方法，构建考虑车辆运行中各类能量损耗的模型。结果表明，基于该物理感知模型的贝叶斯线性回归相比标准线性回归显著提升了能耗预测的可靠性。进一步地，基于同一物理模型的神经网络和梯度提升回归树等复杂机器学习模型在能耗预测上精度更高，明显优于标准版本。此外，我们开发了不确定性估计框架，以预测标准差形式输出预测置信度。所有模型均能合理学习并估计不确定性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。