Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China; Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Central South University, Changsha, 410000, China.
Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin, Berlin, Germany.
J Therm Biol. 2024 Apr;121:103828. doi: 10.1016/j.jtherbio.2024.103828. Epub 2024 Mar 27.
Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.
高速列车（HST）的供暖、通风和空调（HVAC）系统消耗了大约 70%的非运行能源，但它们常常未能为大多数乘客提供足够的热舒适度。最近，便携式可穿戴传感器的进步为实时检测乘客热舒适度状态和及时向 HVAC 系统反馈提供了新的可能性。然而，由于乘客的热舒适度是主观的，无法直接测量，通常是根据 HST 车厢内乘客的热环境参数或生理信号来推断。本文介绍了一项针对 HST 车厢内乘客热舒适度的现场测试。利用生理信号，包括皮肤温度、皮肤电反应、心率和环境温度，我们为 HST 车厢乘客提出了一个预测热舒适度（PTC）模型，并为 HVAC 系统建立了一个智能调节模型。该 PTS 模型共包含 9 个输入因素，包括生理信号、个体生理特征、车厢座位和环境温度。为了获得高效准确的 HST 车厢乘客 PTC 预测模型，我们比较了不同子集的特征通过随机森林、决策树、向量机和 K-近邻的机器学习（ML）模型进行训练的准确性。我们将所有预测特征值分为四个子集，并对每个 ML 模型进行了超参数优化。随机森林模型训练的 HST 车厢乘客 PTC 预测模型的准确率为 90.4%（F1 宏值=0.889）。随后使用 SHapley Additive explanation (SHAP) 和基于数据的敏感性分析（DSA）方法对最佳预测模型进行了敏感性分析。为 HST 乘客开发更准确和高效的热舒适度预测模型，可以为 HVAC 系统提供更精确和详细的反馈。因此，HVAC 系统可以做出最合适和最有效的空气供应调整，提高 HST 乘客热舒适度的满意度，并避免因不准确和不及时的预测反馈而造成的能源浪费。
