## 3 Results
### Thermal Comfort Models  
The PMV model, also known as \"static\", was developed by [PERSON] in the 1970s based on the concept that an individual is comfortable when the body is in heat balance, without the need for excessive intervention by physiological thermoregulatory mechanisms (skin temperature and sweat rate within acceptable limits). Based on a large number of experiments, [PERSON] developed a correlation between the predicted mean vote (PMV) of a group of occupants and the value of some environmental parameters and personal factors--namely, air and mean radiant temperature MRT, relative humidity RH, relative air velocity \(v_{a}\), metabolic rate \(M\), and clothing insulation \(I_{\text{cl}}\). Thermal neutrality is predicted with PMV = 0, with \(-3\) and \(+3\) endpoints of the scale indicating a predominantly cold and hot thermal sensation, respectively, within the group of occupants. As a consequence, a certain percentage of people dissatisfied (PPD) can be expected in a range between 0% and 100%, where 0% corresponds to PMV = 0 and 100% corresponds to either of the the thermal sensation scale endpoints. As noted by [PERSON]'s \"Forty years of [PERSON]'s model of thermal comfort\" [27], the PMV model as developed by [PERSON] is still the most widespread approach to estimate thermal comfort in buildings, despite some known limitations that [PERSON] et al. [28] described with reference to its practical applications to HVAC control. For example, the model is shaped on statistics over a large population, therefore it represents an average indication that often does not match individual preferences. Moreover, it does not take into account the adaptation mechanisms of the occupants, which are especially important in naturally ventilated contexts. Finally, its predictive accuracy strongly depends on the correct estimation of the model inputs, some of which may be too difficult or too expensive to measure in practical applications. To address such limitations, the response of researchers in the years has been twofold: on the one hand, attempts have been made to modify the original PMV framework by simplification or tailoring actions; on the other hand, completely different models have been introduced that do not stem from PMV, such as adaptive and data-based approaches. Figure 7: Keyword co-occurrence network. In this work, the subdivision outlined by [PERSON] et al. [29] between PMV and non-PMV approaches was adopted to categorize thermal comfort models. PMV models include [PERSON] formulation and modified models sharing the theoretical foundation with the original study, leaving any other approach to the non-PMV category. Document breakdown according to the thermal comfort model is shown in Figure 8. In most of the studies, PMV is the preferred way of evaluating thermal comfort. The reasons may be that it is mature, standardized, and agreed upon by the scientific community, thus it is adopted also by researchers without long-term expertise in thermal comfort field. Among the non-PMV models, the availability or large amount of measured information makes data-driven approaches attractive. Automatic inference techniques based on occupants' actions are also gaining interest in a user-centric perspective. Adaptive models are a popular choice with natural ventilation, or when simple and flexible formulations are needed. The boundary between thermal comfort model types is not sharp. To a certain extent, models based on occupants' actions can also be considered data-driven. The same holds for models included into the \"other comfort models\" group based on regression analysis. Additionally, some studies adopted more than one thermal comfort model to compare performances or to describe comfort in different operation modes (Table 3). Thermal comfort modeling approaches in the reviewed papers are discussed in the following paragraphs. **PMV models. ** The majority of the studies reviewed in this survey used PMV models (63%), about one-fourth of which were in modified forms with respect to the Fanger formulation.