# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
###### Abstract  
In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers' thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 degC and 32.0 degC. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift. electric buses; thermal comfort; machine learning; classification +
Footnote †: journal: _aciences_  
e-buses with the goal of improving the overall energy efficiency, while guaranteeing a good thermal comfort to the passengers. This can be achieved by modeling the dependency of human thermal sensation on the climatic conditions inside the bus cabin, thereby enabling a prediction of passengers' thermal comfort. Thermophysiological models [6] are widely employed to assess human thermal comfort in closed environments, such as buildings [7] and vehicles [8; 9]. Most of these models operate by computing the heat exchange between the human body and its surroundings based on climatic (e.g., air temperature, relative humidity and velocity) and personal parameters (e.g., metabolic rate, height, weight and thermal insulation of clothing) [6]. Among the several thermal comfort models developed since the early 1970s, the most widely employed is the PMV-PPD model [6; 10; 11]. This model enables us to compute the comfort of a group of subjects by means of two parameters, namely the predicted mean vote (PMV) and the predicted percentage of dissatisfied (PPD). Whereas the first parameter predicts the average thermal sensation based on the ASHRAE scale for the evaluation of passengers' thermal sensation (TS), the second estimates the percentage of test subjects who experience the environment as thermally unpleasant. The PMV-PPD model is known to provide reliable results in uniform, steady-state climatic conditions (such as those encountered in a climatic chamber). However, the prediction accuracy can decrease significantly in more realistic environments characterized by dynamic conditions. For example, an analysis performed by applying the PMV-PPD model to the ASHRAE Global Thermal Comfort Database II has shown an accuracy of only 34% and a mean absolute error of one unit on the thermal sensation scale [12]. The prediction performance may be even lower for highly dynamic environments such as those encountered in buses. In this case, the climatic conditions can vary greatly depending on, e.g., the proximity to entrance doors or the height above the ground. Additional variations can ensue with time due to changes in outer temperature, solar radiation through the windows and external air flow through the doors [3; 5; 13].