Search

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

[Display omitted] • A machine-learning model is constructed based on experimental data to predicate the gas-sensing performance of oxides. • Six important features are proposed as the descriptors for gas sensing of oxides. • The active sites on oxides for gas sensing are described as ionic oxygen loosely bonded to metal atoms. Machine learning, a new research paradigm, is promising for device design, material discovery, and theoretical exploration, overcoming the drawbacks of experimental approaches. In this study, we used machine learning to explore the gas-sensing reactions of metal oxides. The training data were experimental results. Three types of features — metal oxide and gas molecule properties and operating conditions — were extracted as inputs for the machine learning algorithms. The genetic algorithm-adjusted artificial neural network achieved the best performance, and the neural network test was performed using two new experimental datasets. Further, Shapley Additive exPlanations were employed to understand the findings and explore key descriptors for gas sensing. The feature importance was ranked, and the first six features were selected as key descriptors for gas sensing using metal oxides. Furthermore, theoretical investigations were conducted, and the correlations between features and outcomes were analyzed. We found that the active site for gas sensing on pristine metal oxides comprises ionic oxygen loosely bonded to the metal sites. This study provides an example of the use of machine learning for material discovery. We expect that this study will encourage more studies on machine learning for gas sensing. [ABSTRACT FROM AUTHOR]