# Integration of the Adaptive Approach in HVAC System Operation: A Case Study
## 3 Results and Discussion  
In this case study, the results of the evolution of a room in a building in terms of thermal comfort are shown, considering an approach that combines passive actions and an HVAC system with an adaptive law. It is based on the idea that it is feasible to maintain thermal comfort conditions for a significant percentage of the time using only passive actions, leading to the use of HVAC systems for specific moments by adopting the concept of mixed mode \"change-over\". In this context, users play a key role, as they can express their thermal needs without necessarily knowing the building's predefined setpoint temperatures. In Figure 5, the adaptive model and the declared thermal preference are presented over the course of a year. Human preference and physical conditions determine thermal comfort and its variation over time, with the year being a relevant period of interest, as seasonal [46] or climatic factors [47] can influence preferences regarding cooling and heating. It can be observed in Figure 5 that during 93.49% of the hours, users find themselves in a neutral situation or with undeclared thermal acceptability, 86.43% of the assessments are situations of thermal acceptability without the need of the HVAC system (slightly cooler to slightly warmer), and 13.57% of the situations represent a significant thermal preference change that requires the use of the HVAC system as an adaptive measure. This distribution suggests that during most of the working hours, users are in conditions of comfort or thermal acceptability without the need for adaptive measures. However, 10% of situations require actions to maintain thermal comfort, highlighting the importance of passive strategies or the optimization of HVAC systems during critical periods. Although, based on the adaptive law, the annual variation in the comfort range is observed, the study area during the summer or winter periods shows temperatures outside the range during the working period, due to the influence of the external climate to which the building adapts. When analyzing the results in Figure 6, where the terms TC, FC, TD and FD are associated with the metrics shown in Equations 2 to 6, it can be observed in Table 4 that a PD of 6.5% during the work year and an accuracy of 92.96% have been obtained. This indicates that the model correctly identifies 92.96% of cases in which thermal comfort is predicted within the comfort range. This high value suggests that the proposed temperature range for thermal comfort is quite representative of the real conditions perceived as comfortable by the occupants. It is important to note that the proportion of TC, FC, TD and FD varies throughout the year due to seasonal factors. In particular, from July to September, a higher level of thermal discomfort (TD and FD) is observed. This is mainly due to temperature characteristics during these months, which can exceed the limits of the proposed thermal comfort range. The recall, 82.52%, shows that the model correctly identifies 82.52% of cases in which thermal comfort was actually experienced within the defined range. Although this value is lower than the accuracy, it remains high, indicating that the model identifies a significant proportion of real comfort cases, although it may miss some (false negatives). It is also important to note that although the recall value, 82.52%, could reflect a certain omission of \begin{table}
\begin{tabular}{c c} \hline
**Metric** & **Results** \\ \hline PD & 0.0651 \\ Precision & 0.9296 \\ Recall & 0.8252 \\ Accuracy & 0.7781 \\ F1 & 0.8743 \\ \hline \end{tabular}
\end{table} 
Table 4: Metrics. comfort cases, such a result is directly related to the use of a more restrictive comfort range in this study, defined as +2/ -3 \({}^{\circ}\)C. While the UNE-EN 16798-1 standard also considers a wider comfort range (+3/ -4 \({}^{\circ}\)C), the narrower range was intentionally selected to evaluate the model under stricter conditions.