## 3 Results
### Thermal Comfort Models
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Comfort Model** & **Input Parameters** \\ \hline \multirow{3}{*}{[PERSON] et al. (2017) [34]} & Linear PMV model based on & \\  & regression analysis of & Room air temperature; supply \\  & experimental measurements; & air flow rate \\  & \(M=1\) met, \(I_{\text{cl}}=0.57\) clo & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] (2018) [35]} & Linear PMV model based on & \\  & regression analysis of measured & \\  & environmental parameters; & \\  & \(M=1.2\) met, \(I_{\text{cl}}=0.5\) clo & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] (2018) [36]} & Quadratic PMV model based on & Fan blade pitch; fan speed; \\  & regression analysis & outdoor air temperature; \\  & relative humidity \\ \hline \multirow{3}{*}{[PERSON] et al. (2019) [37]} & Linear PMV model by & \\  & [PERSON] et al. (2013) [38]; & Ambient temperature; relative \\  & coefficients depending on & humidity \\  & gender and clothing insulation & \\ \hline \multirow{3}{*}{[PERSON] et al. (2019) [39]} & Adaptive PMV from [PERSON] et al. & \\  & (2009) [40] & PMV; adaptive coefficient \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [41]} & Quadratic PMV models for & \\  & winter (\(I_{\text{cl}}=1\) clo) and summer & Indoor temperature; air \\  & (\(I_{\text{cl}}=0.5\) clo), \(M=1.1\) met, & velocity \\  & RH = 50\% & \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [42]} & Linear PMV model from & \\  & linearization of the original & Indoor air temperature; \\  & model; \(M=1.2\) met, \(I_{\text{cl}}=1\) clo & absolute humidity \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [43]} & Linear PMV model based on & \\  & multi-linear regression, & Indoor air temperature; air \\  & \(M=1\) met, \(I_{\text{cl}}=1\) clo & velocity \\ \hline \multirow{3}{*}{[PERSON] et al. (2021) [44]} & PMV model by [PERSON] et al. (2018) & Mean room temperature; \\  & [45]; \(M=1\) met, \(I_{\text{cl}}=0.57\) clo & mean airflow velocity \\ \hline \multirow{3}{*}{[PERSON] et al. (2021) [46]} & Linear PMV model by [PERSON] et al. & Indoor air temperature; mean \\  & (2018) [47]; \(M=1\) met, & radiant temperature; absolute \\  & \(I_{\text{cl}}=0.57\) clo, \(\varphi_{\text{ro}}=0.136\) m/s & humidity \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Modified PMV models. calibrated models [55; 56; 57; 58], energy simulations [59; 60], or measurements [49; 61]. Occasionally, assumptions are made also on air velocity and relative humidity, although the latter is generally easy to measure with standard sensors, often in combined temperature/humidity measurement devices. **Non-PMV models. ** The pie chart on the right in Figure 8 shows that almost half of the 61 papers adopting non-PMV approaches are data-driven. For the sake of brevity, only journal articles are reported in Tables 5 and 6 where inputs, outputs, and data-driven algorithms are summarized. Measured, historical, or literature data have been generally used to build models that can predict thermal comfort subjective indicators (sensation, preference, or satisfaction) or, less frequently, comfort-related parameters such as neutral temperature or mean radiant temperature. For the full list of relevant contributions, which includes the articles in Tables 5 and 6 plus eight conference papers, the reader is referred to the  
Adaptive thermal models are the second non-PMV category for number of papers. The adaptive approach is founded on the evidence that the static method tends to overestimate discomfort range in naturally ventilated buildings, especially when the occupants can act on the surrounding environment and adapt it to their preference [80]. The theory thus relates comfort operative temperature to outdoor environmental conditions, generally in the form of a linear correlation. The single dependent variable was initially taken as the monthly average of outdoor air temperature ([PERSON], 1978 [81]), and subsequently replaced with an exponentially weighted running mean (EWRM) to include the memory of weather history.