## 1 Introduction
### Related Research
#### 1.1.1 Thermal Comfort and Climate Change  
Thermal comfort in general and the [PERSON] [11] model in particular have been used in different simulation scenarios, but oftentimes for a specialized purpose or situation, such as thermal comfort in trains [12], the prediction of thermal comfort in indoor swimming pools [13], the heat exposure in a kitchen environment [14], or the investigation of rural heating systems' efficacy [15]. In early design stages, suitable models for occupant behavior allow the designers to evaluate design decisions with respect to their combined influence on comfort and the building's energy consumption [16]. [PERSON] and [PERSON] [17] describe a simulation approach where BPS and HVAC system simulations were coupled with Computational Fluid Dynamics (CFD) in order to investigate the feasibility of Net-Zero Energy concepts in office buildings. Although their approach is quite extensive, including thermal and draught simulation as well as prediction of CO\({}_{2}\) distribution, it might prove beneficial to interface this approach with IFC to facilitate integration into the BIM process. [PERSON] et al. [18] describe an approach to investigate the vulnerability of Portuguese residential buildings, focusing on occupancy and insulation. Their multi-step procedure involves geometry and constructive data acquisition, acquisition of monitoring data, modeling in EnergyPlus as well as calibration against weather data and the final comfort assessment. The results emphasized the beneficial role of external insulation in increasing the adaptive capability of buildings. As stated by the authors, future replications should also include air-tightness of the building or additional shading devices in the simulation. Applying the Simulation-based Large-scale uncertainty/sensitivity Analysis of Building Energy performance (SLABE) methodology, [PERSON] et al. [19] projected the impact of climate change on a building category with a case study object located in Seville, Spain. Generalization took place by Monte Carlo resampling of the EnergyPlus input parameters. Among the nine geometrical parameters, floor height, form ratio, floor area, and orientation had considerable influence. Among the 17 envelope parameters, the absorptance of the wall's external layer showed the highest standard rank regression coefficient in that category. User behavior was identified as the major influence within the three analyzed operation parameters, but also among all categories, and its relevance was estimated to even increase with the progress of climate change. In line with previous studies, the authors project an overall worsening of the indoor thermal conditions towards the year 2050, resulting in elevated cooling demands (up to 250 % increase) and an increase in discomfort hours of about 36 %. [PERSON] and colleagues suggest and emphasize the usefulness of incorporating the approach as a plugin in BIM tools. Aiming to improve multi-type building performance prediction as well as optimization, [PERSON] et al. [20] propose a machine learning (ML)-based procedure, which was applied to dwellings by the Singapore Housing Development Board. Transfer learning techniques, as well as multi-objective genetic optimization, were utilized to derive optimal performance and design parameters for daylight performance, energy efficiency, and thermal comfort from short to long-term future climate conditions. Various approaches to evaluate the thermal resilience of buildings were reviewed by [PERSON] et al. [21]. According to the authors, one of the main issues to be resolved is a lack of standardized procedures for the prediction and evaluation of a building's thermal resilience with simulations. This holds true not only for the BPS part but also for the methods applied to create extreme weather data. Many of the studies projecting thermal comfort changes caused by climate change oftentimes focus on regions where heat is already present [22; 23; 24].