# Integration of the Adaptive Approach in HVAC System Operation: A Case Study
The adaptive comfort control approach could be seen as a solution to the phenomenon described by [PERSON] [26] in which occupants of buildings with traditional HVAC systems develop an expectation of thermal constancy due to the strict maintenance of fixed temperatures. This expectation, as the author points out, causes even small fluctuations in temperature to be perceived negatively, generating a hypersensitivity that leads to frequent complaints. Automated controls that adjust HVAC setpoints according to the outdoor climate and seasonal variations can be an effective solution. In fact, [PERSON] and [PERSON] [27] suggest that increasing indoor climate variability is key to sustainable building design strategies. In this context, it becomes relevant to analyze mixed mode (MM) buildings, which combine natural ventilation and HVAC systems. Although there is no consensus in the literature on control strategies for mixed mode buildings [28], this study proposes the analysis of a real case of integration of the adaptive approach in the operation of HVAC systems to investigate the implementation of a real mixed mode situation. Given that an MM building does not have to operate as a fully conditioned building the whole time, and although some authors consider or analyze that they must operate in the HVAC mode above a certain temperature [27], the reality is that an MM building can be a free-behaving building. The hypothesis analyzed in this study is that operating a significant percentage of the time in passive mode, with the HVAC system serving as a temporary backup adaptive measure, would be a feasible solution. This hypothesis is supported by recent findings [29], although only 20% of case studies on adaptive setpoints in Building Automation Systems (BAS) have been implemented in real-world cases (in European countries, this accounts for 36% of the 20%), and despite the fact that the operational strategies in mixed mode buildings are suitable to such approaches (48% are categorized as changeover strategies). Therefore, the aim of this paper is to expand upon these studies, addressing the gap in literature by analyzing real-world feasibility. Moreover, in recent studies such as [30], it has been shown that in situations when extreme outdoor temperatures entail the use of HVAC systems, adjusting adaptive setpoints can lead to a reduction of approximately 35% in cooling demand, thereby contributing to both energy savings and greater thermal comfort. The user might rely on this measure (HVAC usage) without being aware of the building's setpoint (due to its adaptive nature), thus, expressing its need to improve the temperature on an ad hoc basis. Consequently, the setpoint, as it is adaptive, would follow the temperature proposed by the adaptive comfort algorithm, with the consequent energy savings that this would entail, compared to a continuous conditioning operation based on a fixed setpoint. In relation to the number of field studies carried out in MM buildings, although a growing trend of research focused on this area can be detected in the literature, the number is lower than in naturally ventilated or fully conditioned buildings [29]. In terms of climatology, building type and HVAC systems, a higher volume of work can be identified in MM buildings in oceanic and humid sub-tropical climates, commercial or business buildings and change-over and concurrent operations strategies, respectively. There is also a wide variety of control algorithms developed for MM buildings. Although some studies propose rule-based control, considering variables such as outdoor temperature, indoor temperature, thermal comfort or adaptive behaviors, among others, other research focuses on complex computational controls based on fuzzy logic or machine learning algorithms, highlighting the diversity of approaches in this field. Additionally, there is a need for this type of case study, since, as indicated by [29], 80% of the case studies are based on simulations, which points to the need to analyze a larger number of real implementations.