## 3 Results and Discussion
### Effect of the Increase in Tolerance in the Current Scenario  
By analyzing the results of the current scenario, it was found that the level of users' thermal expectations progressively increased in the building energy consumption. Figure 7 shows that the distribution of the annual results of energy consumption in all the cities analyzed progressively increased both consumptions (heating and cooling). Furthermore, such increase led to an energy  
Figure 5: Hourly distribution of loads. Figure 6: Flow chart of the simulation process. consumption more like that of the static model, even obtaining a greater energy consumption. In this regard, up to a tolerance of 1.50 \({}^{\circ}\)C, average savings were achieved in the total energy consumption, whereas higher tolerances implied a greater energy consumption than the static model (see Table 4). To understand this effect, it is fundamental to analyze the energy saving obtained by adaptive models in each type of consumption. Figure 8 represents the distributions of energy-saving data obtained with respect to the static model. The distributions had different tendencies according to the type of energy consumption, whereas in heating, negative values of energy saving were achieved from the adaptive model of tolerance of 1.00 \({}^{\circ}\)C in some cities, while some negative values were not \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tolerance** & \multicolumn{3}{c}{**Percentage Deviation (\%)**} \\ \hline  & \multicolumn{3}{c}{**Current**} \\ \cline{2-4}  & **Cooling** & **Heating** & **Total** \\ \hline
0.00 & 68.90 & 26.48 & 38.74 \\
0.25 & 64.32 & 21.34 & 33.67 \\
0.50 & 58.73 & 16.01 & 28.77 \\
0.75 & 52.68 & 10.42 & 23.34 \\
1.00 & 45.89 & 4.58 & 17.65 \\
1.25 & 38.34 & \(-\)1.47 & 11.70 \\
1.50 & 29.93 & \(-\)7.79 & 5.47 \\
1.75 & 20.57 & \(-\)14.34 & \(-\)1.03 \\
2.00 & 10.09 & \(-\)21.09 & \(-\)7.82 \\
2.25 & \(-\)1.72 & \(-\)28.06 & \(-\)14.93 \\
2.50 & \(-\)15.23 & \(-\)35.20 & \(-\)22.40 \\
2.75 & \(-\)31.14 & \(-\)42.59 & \(-\)30.42 \\
3.00 & \(-\)51.01 & \(-\)50.46 & \(-\)39.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage deviation between the energy consumption of the adaptive model and the consumption of the static model in the current scenario. Positive values correspond to a decrease in energy consumption, and negative values correspond to an increase. Figure 7: Box-plots with the energy consumption distribution obtained in all cities with each configuration of static setpoint temperatures (current scenario). achieved in cooling consumption until the model of tolerance of 2.00 \({}^{\circ}\)C. Two fundamental aspects are found: (i) the possible limitations of heating adaptive setpoint temperatures with static temperatures when correlations of determination of such limit obtain temperatures greater than 20 \({}^{\circ}\)C. In this sense, the increase in demand level with heating temperatures could hinder the potential energy saving that could be obtained with these setpoint temperatures. (ii) The cooling temperature leads to a greater tolerance in which guarantees a greater application of these measures without influencing the thermal comfort or the energy saving achieved. It is important to stress that cooling consumptions are based on the period considered to use cooling systems, so the percentage contribution of cooling consumption with the total could be greater. Regarding the total energy consumption, as the heating contribution in the results obtained was greater than the cooling contribution, the tendency found in the total energy consumption was similar to the heating consumption, obtaining negative results in the tolerance of 1.75 \({}^{\circ}\)C. In this type of energy consumption, the increase distribution of the energy consumption raises from a tolerance of 1.75 to 3.00 \({}^{\circ}\)C. Thus, energy increase distributions ranged between 338.78 and 1583.98 kWh/year, instead of between 23.73 and 356.39 kWh/year.