## 3 Results
### Thermal Comfort Models
Adaptive model is included in the reference standards [4,6] and its use is recommended only in the case of naturally ventilated buildings and within a limited outdoor temperature interval. However, the recent literature is exploring adaptive model potentiality also in case of mixed-mode or mechanically ventilated buildings, as can be observed in Table 7. The mathematical formulation is quite simple to implement and requires only one type of measurement (outdoor temperature). Moreover, experimental measurements and thermal comfort surveys can be carried out to calibrate the coefficients of the adaptive formulation to a specific location, building, or group of people. A third category of non-PMV-based models is worth mentioning: the automatic inference of thermal comfort preferences from users' interactions with control devices, made possible by available current technologies. Table 8 reports the papers characterized by this approach. Some predominant features can be identified: the use of machine learning techniques, the personalized connotation of this approach, and the preliminary stage of the works (five out of eight conference proceedings, mainly theoretical or simulation-based). The solutions developed with this class of methods are difficult to generalize, in that there is no underlying model and, differently from data-based approaches, the preferences are inferred and not asked directly. For example, [PERSON] et al. [82] exploited occupants' actions on the thermostat, and decided to use only two of the three pieces of information that can be extracted from them (discomfort condition and direction of corrective action), because the temperature set by the user cannot be assumed as the ultimate preferred value. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Inputs** & **Outputs** & **Algorithm** \\ \hline [PERSON] et al. (2020) [73] & Indoor temperature and humidity & Thermal comfort value & Feedforward neural network \\ \hline [PERSON] and [PERSON] (2020) [74] & Personal factors and indoor environmental parameters & Comfort coefficient & Neural network \\ \hline [PERSON] et al. (2020) [75] & Biometric data, environmental data, comfort feedback & Thermal comfort level & Decision tree, adaptive boosting, gradient boosting classifier, random forest, support vector machine \\ \hline [PERSON] et al. (2020) [76] & Preferred temperature via app & Individual temperature preference & Value provided directly \\ \hline [PERSON] and [PERSON] (2020) [77] & Thermal sensation feedback and environmental measurements and physiological parameters & Thermal sensation & Artificial neural network \\ \hline [PERSON] et al. (2021) [78] & Thermal sensation and thermal satisfaction feedback, heart rate, and wrist skin temperature and its variation & Thermal sensation & Linear regression \\ \hline [PERSON] et al. (2021) [79] & Thermal comfort feedback, environmental indoor and outdoor measurements, clothing level, HVAC equipment states & Thermal sensation and thermal satisfaction & Random forests, k-nearest neighbors \\ \hline [PERSON] and [PERSON] (2021) [44] & Classified garment image database; thermal sensation vote feedback, air and face temperature & Clothing level classification, comfortable air temperature & Convolutional neural network \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data-driven thermal comfort models in reviewed journal articles (2020–2021). In addition to the previous three categories, other non-PMV approaches found in the analyzed papers include thermal comfort predictions based on physiology and sensation models [111], evaluation of discomfort degree-hours according to the preferred approach [112], rule-based indicators [113], direct preference from the user [114], and literature-based or self-developed sensation and comfort indicators [115; 116].