# Integration of the Adaptive Approach in HVAC System Operation: A Case Study
## 3 Results and Discussion
This approach provides valuable insights into the trade-offs between adhering to high comfort standards and achieving energy efficiency. The accuracy, which measures the overall percentage of correct predictions of all predictions made, is 77.81%. This result shows that the model performs acceptably in general when making predictions for all possible responses of the occupants. Finally, the F1-Score, which balances precision and recall, is 87.43%. This value is relevant because it combines the model's ability to correctly identify comfort cases (recall) and avoid incorrect predictions (precision). A high F1-Score suggests a good balance between these factors, ensuring the reliability of the model, both from the perspective of occupant thermal comfort and potential energy savings. The adaptive model implemented and integrated into the BMS has proven to be effective when improving thermal comfort and presents considerable energy-saving potential. When analyzing the hours of HVAC system usage throughout the year (13.57% in global terms), it is observed that most thermal discomfort occurs during July and August, as shown in Figure 7a, along with a significant increase in HVAC system usage, according to Figure 7b. It is important to note that the data is presented in percentage terms. During the month of July, university activity is mainly concentrated in the morning and it extends into the hottest hours of the day, when outdoor temperatures reach their maximum value, entailing an increase in HVAC system usage. In the case of August, although the building had very few days of occupancy due to the vacation period, HVAC system usage remains relevant to ensure thermal comfort on specific days of activity, where the HVAC system is employed as a complement to natural ventilation to maintain the thermal environment within acceptable limits. The \"change-over\" mixed mode suggests that it is feasible to operate a significant percentage of the time using only passive ventilation, with conditioning systems serving as backup at certain moments. The presented metrics allow for an analysis of the actual behavior after applying the adaptive comfort law as a guideline and as an adaptive action, while the office space operates under free behavior, meaning the user has the freedom to take adaptive actions such as using the HVAC, opening windows, or changing clothes. The MM building functioned as a passive building, temporarily accepting temperatures between 18 and 30 degrees. In the case study, if users were uncomfortable, the system acted to improve user temperature within the adaptive comfort range for a maximum  
Figure 7: **(a)** Discomfort hours; **(b)** HVAC use, compared to the average monthly outdoor temperature. of 2 h, with the adaptive guideline as the objective. The results suggest that occupants tend to feel slightly better during an NV-AC mode change event, but do not require high use of the HVAC systems as a thermal solution. In this case study, a period of 10% of the year sufficed. Figure 5 shows that it is possible to allow indoor temperatures to vary within acceptable limits based on adaptive comfort, being more energy efficient while still ensuring thermal comfort for the building's users. In case comfort limits are exceeded, occupants tend to use the HVAC system, but it can be turned on intermittently to maintain temperatures within the standards of adaptive comfort. In some cases, users did not activate the system, but the width of the range and the comfort law appear to be a more appropriate alternative compared to the fixed setpoints of a controlled HVAC system. The results obtained align with those stated by [26; 48] who stated that \"_Naturally ventilated buildings afford their occupants greater degrees of thermal control than air-conditioned buildings, and that this sense of control leads to a relaxation of expectation and greater tolerance of temperature excursions_\" (p. 17). The same criteria are proposed in the present study by relaxing expectations to a setpoint based on adaptive law.