## 3 Results
### Putting It All Together: Thermal Comfort Control Systems
Among \"simple\" thermal comfort models, the adaptive comfort model was used in all the analyzed studies with simulations or prototypes characterized by natural ventilation  
Figure 14: Qualitative illustration of thermal comfort model and control system complexities in the analyzed papers. The distribution of points within quadrants is random. Figure 13: Thermal comfort model category at varying control algorithms and levels of readiness. Percentages are relative to the total number of papers considered in either chart (i.e., with the specific level of readiness). (Figure 15). However, it was also adopted in a significant number of cases with air conditioning or hydronic systems. In case of relatively unsophisticated appliances, such as personal or electric heaters, PMV and data-based approaches were preferred. Figure 16 shows the breakdown of works making use of ML in the definition of thermal comfort models and indoor environment controls over the years. The exploitation of this family of techniques grew steadily until 2020, whereas the trend reversed in 2021. Though the sample is too small for accurate conclusions, COVID 19 pandemic effects may have had a role in this anomaly from two points of view. On the one hand, the requirement for machine learning is the availability of data, which might have been more difficult to collect in 2020 than in previous years (hence the brake on ML-driven works in 2021). On the other hand, the pandemic caused a change of habits in various fields [159], and it may take longer to process and understand data collected in this period; this may have been the cause of a publication delay. There is no clear correlation between the use of ML techniques, the thermal comfort model category, and the level of readiness (Figure 17). It can be noted that in simulation works, ML was mainly used in control system development, while in prototypes it was often adopted in thermal comfort assessment. This confirms the trends illustrated in Figures 13 and 14. Figure 16: Use of ML in comfort models and control algorithms through the years. Figure 15: Type of comfort model by HVAC type (level of readiness simulation or prototype). In terms of field of application, a large portion of the papers focused on commercial and office buildings (Figure 18), for two main reasons: a usually higher level of readiness of the control and management infrastructure, and the driving force of attractive energy savings. In this respect, there is a correlation with the predominant number of AC systems and of cooling applications, which are typical of tertiary facilities. Residential and educational buildings are also investigated frequently, while few studies focus on thermal-comfort-based control of indoor recreational, social, guest accommodation, and healthcare environments. The last are delicate facilities, in that their occupants include vulnerable people with special needs that must be taken into consideration (see Section 4). With reference to shared spaces, the problem of evaluating thermal comfort for multiple occupants can be of relevance, and has been tackled in several studies. Three ways of satisfying individual preferences can be identified:  
1. With personal devices, such as desk fans;
2. By providing thermal comfort models with \"average\" inputs representing the occupants, for example through machine learning techniques;
3. By collecting individual thermal preferences and applying decision algorithms. The first category is the actual expression of personalized thermal comfort paradigm, but it is not always applicable or energetically convenient. The second approach uses \"average\" information to describe a group of people; thus, its modeling capabilities depend on the quality of input data and on the homogeneity of the occupants. The third is an intermediate option, in which individual preferences are computed and synthesized into group settings. A summary of studies using this approach is given in Table 10.  
Figure 17: Use of ML by comfort model category and level of readiness.