# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
## 5 Conclusions and Next Steps  
As cities start to electrify their public transport systems as a measure to tackle climate change and air pollution, a more efficient way to operate these becomes of mayor importance. For this, HVAC system is of high relevance, as it may use up to 50% of the battery capacity. This leads to the question of efficiently conditioning bus cabin environment without compromising the comfort of the passengers. Until recently, the most used model to predict thermal comfort was the PMV-PPD model, that has not only been shown to  
Figure 11: Median accuracy of ML models using the 3 different parameter sets compared to PMV accuracy using parameter set \(S_{1}\). have low accuracies while predicting thermal comfort, but requires measurements that might not be easy to obtain, such as clothing insulation. In this paper, 329 personal surveys were collected during climatic measurements on an electric bus in Berlin, Germany and 278 of them were used for this assessment. Seven of the most typically used ML models for thermal comfort prediction were trained and tested 100 times with repeated random sub-sampling, using three different parameter sets of the dataset. The \(F_{1}\) score was used as an accuracy metric. The results were then compared to the PMV-PPD model. The comparison showed that it is possible to achieve similar or better results compared to the PMV-PPD model using machine learning, if the PMV value is adjusted by +1. It is even possible to obtain better results with only two input parameters (\(T_{a}\) and \(RH\)) compared to the five that are needed for PMV-PPD model, even using a very small set of 278 data points for input. This would drastically simplify the measurement of thermal comfort in the bus cabin. To further prove this requires a crucial next step, which will be the collection of a larger dataset that includes measurements throughout the year (in cold and warm environment), from which a multi-label classification of the whole ASHRAE scale could be achieved. From the shift analysis in Section 3.1, it can be concluded that the PMV-PPD model tends to underestimate extreme TS and instead predicts values closer to neutral TS. Further measurements will also be needed to corroborate if the PMV shift toward neutral values that were found in the data set, is related to the time the passengers spent in the bus. For this, two surveys could be filled, one shortly after entering the bus and another after spending some time. If the shift is reduced in the second survey, it could be concluded that time and acclimatization are of importance when calculating PMV values. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132011190/s1](https://www.mdpi.com/article/10.3390/app132011190/s1)]([https://www.mdpi.com/article/10.3390/app132011190/s1](https://www.mdpi.com/article/10.3390/app132011190/s1)). \(S_{3}\) encoding selection, Precision-Recall Curves, Receiver Operating Characteristic Curves as requested by the reviewers. Conceptualization, T.-A.F., F.C. and A.S.A. ; methodology, T.-A.F., F.C. and D.G. ; software, A.S.A. and F.C. ; validation, T.-A.F., F.C. and A.S.A. ; formal Analysis, T.-A.F. and A.S.A. ; investigation, A.S.A. ; resources, D.G. ; data curation, F.C. and A.S.A. ; writing--original draft, A.S.A., T.-A.F. and F.C. ; writing--review and editing, T.-A.F., A.S.A., F.C. and D.G. ; visualization, A.S.A. ; supervision, T.-A.F. ; project administration, T.-A.F. and F.C. ; funding acquisition, D.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the Federal Ministry of Transportation and Digital Infrastructure, Project \"E-Metro-Bus\", grant number 3 EMF0105B. [[https://e-metrobus.berlin/](https://e-metrobus.berlin/)]([https://e-metrobus.berlin/](https://e-metrobus.berlin/)) (accessed on 2 October 2023). We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin.