## 2 Material and Methods
### Methodology
#### 2.2.4 Strategies and Impact Assessments
In particular, hourly values of indoor air temperature collected during the preliminary monitoring campaign were compared with simulated values (the simulated trend referred to the Hall's average trend and for the validation phase it was compared with the average trend of the two levels measured during the preliminary monitoring campaign). This was performed using three indices: mean bias error (MBE), coefficient of variation of root mean square error (CvRMSE)) and Pearson's index (r). The first two indices are suggested by ASHRAE Guideline 14-2002 [44], while the third one (r) is a typical statistical coefficient of linear correlation between the two variables of simulated and measured data [45]. In this study, the CvRMSE index,the normalized mean bias error (NMBE) and the determination coefficient (R2), that is the square of [PERSON]'s index (r), were considered. The model was considered to be calibrated when the mentioned indices matched the values reported in the literature [44]. During the iterative simulation phase, the model was optimized, varying the values attributed to the infiltration air change rate and the air mixing between adjacent zones, which were regarded as the uncertain parameters mostly influencing simulation results [23]. In Table 7, for the Sala del Dottorato, annual values of the NMBE, Cv(RMSE), and R2 indices are reported for the model configuration that presents the best annual value. The interventions were first simulated individually and then combined. In particular, the installation of solar control films was simulated for the windows of the mezzanine level, modifying the properties of the glass: the solar factor was assumed equal to 0.22 and the light transmittance equal to 0.20. In all cases, the effectiveness of the different strategies was evaluated utilizing the indoor temperature trend variation (\({}^{\circ}\)C) over one year. In order to propose management actions for natural ventilation, the evaluation of the effects of opening the windows during events (in terms of CO2 concentration), an empirical method was carried out. The most representative events were chosen in terms of the number of people, duration and season to highlight the most critical conditions. For each event, the following elements were recorded and calculated:  
* _Duration_, the duration interval (min);
* _N. people_, the number of people inside the Hall during the meeting;
* _Initial CO2 level_, the CO2 level at the beginning of the event (ppm), which represents the background level (measured by the sensor);
* _Final CO2 level_, the CO2 level at the end of the event (ppm), which is the maximum value reached (measured by the sensors);
* _vincrease_, the growth speed of CO2 during the event with closed windows (ppm/min);
* _Window opening_, in terms of the interval before the event (min);
* _CO2 level after opening windows_, the CO2 level reached with opened windows (ppm);
* _vdecrease with open windows_, the degrowth speed when the windows were opened (ppm/min);
* _Recovery time of the initial CO2 level with closed windows_, the time taken to return to the initial CO2 level after closing the windows (hours);
* _vdecrease with closed windows_, the degrowth speed to return to the initial CO2 level with closed windows (ppm/min). The dataset was used to define a protocol for opening the windows considering that the curators could open all the windows for air exchange before the end of the event. The aim of the study is to identify a correct value of the CO2 concentration, as a limit to open the windows for air exchange. Eventually, the best value (as a compromise in terms of people, duration and external conditions) is estimated and it is defined as a limit before the onset of bad conditions (values greater than 1000 ppm), beyond which the curators could open all the windows.