Data-driven prediction of vehicle cabin thermal comfort: using machine learning and high-fidelity simulation results

Abstract Predicting thermal comfort in an automotive vehicle cabin's highly asymmetric and dynamic thermal environment is critical for developing energy efficient heating, ventilation and air conditioning (HVAC) systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and machine learning algorithms to predict vehicle occupant thermal comfort for any combination of glazing properties for any window surface, environmental conditions and HVAC settings (flow-rate and discharge air temperature). A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training data that spanned the entire range of boundary conditions, which impact occupant thermal comfort. Three machine learning algorithms: linear regression with stochastic gradient descent, random forests and artificial neural networks (ANN) were applied to the simulation data to predict the Equivalent Homogeneous Temperature (EHT) for each passenger and the volume averaged cabin air temperature. The trained machine learning models were tested on unseen data also generated by the CFD model. Our best machine learning model was able to achieve a test error of less than 5% in predicting EHT and cabin air temperature. Predicted EHT can also yield thermal comfort metrics such as Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), which can account for different passenger profiles (metabolic rates and clothing levels). Machine learning models developed in this work enable predictions of thermal comfort for any combination of boundary conditions in real-time without having to rely on computationally expensive CFD simulations.