## 3 Results and Discussion
### Artificial Neural Network for \(\mathit{CO_{2}}\) Concentration Prediction  
According to Section 2.4 and to the adopted implementation procedure (Figure 4), several ANNs were trained by varying the number of neurons to be used in the hidden layers. For the sake of brevity, only the results of the best trained ANN will be reported. The best ANN was obtained by using 173 neurons in the hidden layer which has the highest regression value (equal to 0.967 for all the training  
Figure 10: Correlation between MAA with opening area and volume. processes) and the smallest MSE (the most probably error is in \(-\)50-50 ppm range). The trained ANN was then tested for the CO\({}_{2}\) prediction with the other experimental campaigns, as shown in Figure 4.  
Results related to the implementation of the network are shown in Figures 11 and 12. In particular, Figure 11 shows the comparison between the CO\({}_{2}\) values monitored during the experimental campaigns (on the abscissa--target) and the one simulated by ANN (on the ordinate--ANN output) for both the considered processes (training in black dots and testing in red box). In this figure, in order to highlight the accuracy of the ANN, the regression line for each process was also drawn: R\({}^{2}\) values equal to 0.98 and 0.99 were found for the training and testing processes, respectively. Figure 12 allows to confirm the reliability of the trained ANN; the MSE in the two processes (training in blue bars and testing in red ones) is reported. Particularly, it shows that the most probably error returned by the ANN is in the \(-\)50/50 ppm range (84.7% of cases) for the training process, and \(-\)100-100 ppm range (84% of cases) for the testing one. According to the results, the trained ANN can be considered reliable; so, the simulated CO\({}_{2}\) concentration can be used for the MAA calculation. In Table 5 the comparison of the starting concentration (C\({}_{0}\)--the one monitored or simulated at the time step equal to 0), the maximum CO\({}_{2}\) concentration reached during each survey (C\({}_{\text{max}}\)), and MAA calculated by using ANN output and experimental one are shown. Besides, surveys in red in Table 5 are related to the experimental campaigns used for the testing process, the other ones for the training. It is worth noting that the CO\({}_{2}\) concentration simulated by ANN at time step 0 is always close to the real values; i.e., the different between the two values is always lower than 10 ppm (always lower  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Number of Survey**} & \multicolumn{3}{c}{**Experimental Data**} & \multicolumn{3}{c}{**Simulated Data**} & \multicolumn{3}{c}{**Analysis**} & \multicolumn{3}{c}{**Relative Error [\%]**} \\ \cline{3-10}  & & **C\({}_{\text{max}}\)** & **MAA** & **C\({}_{\text{0}}\)** & **C\({}_{\text{max}}\)** & **MAA** & **C\({}  
than 2%). It means that the ANN allows to correctly simulate the real concentration within the room, starting from the environmental conditions. The maximum concentration simulated by ANN, on the other hand, presents much higher error, with peaks of \(-140\%\). This means that the network is not always able to correctly predict the maximum values of CO\({}_{2}\) reached within the environment. In fact, only in about 50% of surveys an error lower than \(\pm\) 30% was found. However, despite the different maximum value of CO\({}_{2}\) concentration simulated by ANN, the MAA calculated starting from ANN data is very close to the experimental one except for a very few cases (mainly in OD_OW configuration). In this case, the mean error is about 1-2 min. As shown in Table 5, the higher relative error was found in OD_OW configuration, which is the one with the lowest values of MAA; in fact, as also reported in Table 6, the mean values of MAA derived from ANN data differ for a maximum of 2-3 min for OD_OW.