# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
###### Abstract
To better capture these phenomena, dynamic thermophysiological models, such as the two-nodes model [3; 8; 9; 14] or local models [15; 16], are sometimes used. However, the accuracy of such models for bus cabins is unknown due to the limited validation by comparison with empirical results from, e.g., passengers' surveys. Further disadvantages of thermophysiological models include the high computational complexity, which increases when time dependency or local effects are considered. Finally, large sets of climatic and personal parameters are typically required as an input for the computation [10; 14]. This may constitute a significant challenge in cases where only a limited set of data is available from measurements. Because of the aforementioned limitations of thermophysiological models, recent studies have focused on the development of data-driven models based on machine learning (ML) to achieve a more accurate prediction of thermal comfort [17]. These models have demonstrated high performance for the assessment of occupants' thermal well-being in buildings, showing up to 74% higher accuracy when compared to the PMV-PPD model [18]. An additional advantage of ML is its ability to consider any set of input parameters for modeling, thereby enabling us to also include parameters not considered by conventional thermophysiological models (e.g., sex, age and external air temperature) or use less parameters than the thermophysiological models. Because of these advantages, ML has recently been attracting attention to assess thermal comfort in vehicles, such as cars and buses. In [19], for example, three distinct ML models were tested to analyze thermal comfort in passenger cars, showing a high accuracy of up to 96%. However, in this case, the measured body temperature was adopted as an indicator for thermal comfort, thereby ignoring other individual factors and personal preferences that can affect the perception of the surrounding climatic conditions. [PERSON] and [PERSON] employed passengers' surveys to empirically evaluate the thermal well-being of passengers in a bus cabin [4]. The survey data were then combined with measured climatic parameters and employed in a multiple regression analysis for assessment of thermal comfort. However, the modeling performance remains unknown, as no information was given about the accuracy. To overcome the limitations of the aforementioned studies, ML is used in this paper to assess passengers' thermal comfort in an e-bus. For this purpose, a data set obtained from climatic measurements in an e-bus in Berlin, Germany [5] containing both climatic parameters and personal information of 278 passengers is used for modeling. Because of the limited number of input data and the discrete nature of the declared TS, passengers were arranged in two groups representing mild and warm thermal sensation. Classification Models [20] are then used to classify passengers into the aforementioned groups. To identify the best suited model, seven ML classifiers based on different algorithms are designed, trained, tested and compared, using three different input parameter sets of the Berlin data set. Additionally, a further comparison is made with the PMV-PPD model to test the accuracy in respect to thermophysiological models. This paper is organized as follows: in Section 2, the climatic measurements, the data sets, and used models are described. The results of the classifiers and the PMV model are presented in Section 3 and discussed in Section 4. The conclusions are then presented in Section 5.