## 3 Results and Discussion
### Thermal Comfort Levels
On top of that, the number of Discomfortable Non-Applicable Hot Hours was 0 in the SRES scenarios, whereas in the RCP scenarios, the rise of temperature increased this number, although it continued to be lower than the decrease of Discomfortable Non-Applicable Cold Hours. The rise of temperature consequently increased the running mean outdoor temperature. Figure 6 shows this increase in the 25%, 50%, and 75% quartiles, represented by horizontal black lines in each chart. In the cities of Barcelona and Rome, the average of the RMOT in the SRES scenarios increased between 0.5 and 1.1 \({}^{\circ}\)C, and between 0.2 and 0.6 \({}^{\circ}\)C, respectively, whereas in the RCP scenarios, the increase was between 1.9 and 2.9 \({}^{\circ}\)C, and between 2.6 and 3 \({}^{\circ}\)C. In the cities of Berlin, Bern, and Vienna, the average of the RMOT increased between 0.7 and 1.1 \({}^{\circ}\)C; 0.1 and 0.7 \({}^{\circ}\)C; and between 0.3 and 0.7 \({}^{\circ}\)C in the SRES scenarios, whereas in the RCP scenarios, it was between 1.6 and 2.1 \({}^{\circ}\)C; 1.4 and 2.7 \({}^{\circ}\)C; and 2.1 and 2.8 \({}^{\circ}\)C. This increase is also shown in Figures 7-11, which include the cloud points corresponding to the operative temperature, the HST, and the CST in the static and adaptive model, as well as in the different scenarios and cities. As can be seen, all green points, which represent hours, were between the CST and the HST both in the adaptive and static model. As for the static model, there were two heating and cooling setpoint temperatures which corresponded to heating and cooling seasons. A heating and a cooling setpoint temperature corresponded to each hourly operative temperature. In such way, setpoint temperatures were horizontally extended, with operative temperatures so adjusted to the setpoint that they were almost hidden under each setpoint. There were also gaps in HST and CST as there were not any days with such temperature. Figure 7: Operative temperature in the city of Barcelona, based on comfort models and scenarios. Figure 8: Operative temperature in the city of Rome, based on comfort models and scenarios. Figure 9: Operative temperature in the city of Berlin, based on comfort models and scenarios. Figure 10: Operative temperature in the city of Bern, based on comfort models and scenarios. Columns Discomfortable Non-Applicable Hot Hours and Discomfortable Non-Applicable Cold Hours are the number of hours in which the adaptive model is not applicable, corresponding to the hours in which HST and CST are horizontally extended in the adaptive model (Figures 7-11). Nevertheless, it is important to stress that these data only affect the analysis of the applicability of the model, only leading to discomfort when the building does not have active systems. In this research, the active systems were in operation as they were needed during the non-applicable hours, obtaining  
Figure 11: Operative temperature in the city of Vienna, based on comfort models and scenarios. in the worst case 99.65% of hours within the setpoint temperatures (in the RCP8.5 scenario, in the city of Rome). The current adaptive comfort model can assess adaptive thermal comfort levels in the current scenario. However, results regarding adaptive thermal comfort levels in future scenarios is limited as the adaptation to the increasing temperatures has not been considered, because the thermal acceptability and comfort thresholds of human beings might change as they adapt to increasing temperatures caused by climate change. Therefore, in the case that thermal adaptation was considered, energy saving potential might grow, as human beings would consider higher temperatures acceptable, so cooling adaptive setpoint temperatures could be higher than those actually calculated.