## 3 Results
### Impact of Data Quality on ANN Performance
Although the prediction and ground truth lines are generally parallel, indicating a good capture of the heat transfer mechanisms, the absolute maximum error of 32.04 \({}^{\circ}\)C observed in Wall Assembly 3 is a reminder of the inferior performance of this ANN. This is not an outlier, as on average there is a 16.82% error in all observations made by this network. The degraded performance of this network is also reflected by the lower coefficient of determination and higher root mean square error, which on average are 0.9540 (compared to ANN 1's score: 0.9992) and 6.65 (compared to ANN 1's excellent fit score of 0.88), respectively. A graphical observation of the third network's (ANN 3) performance reveals similar trends to ANN 2. The predictive capacity of the model is clearly inferior to ANN 1. However, the general mechanism of the heat transfer process has been captured, as indicated by the fact that the ground truth and prediction curves are approximately parallel. The distance between the two lines is larger than observed in the case of ANN 2. It is also worth noting that the curves generated by the ANN 3 predictions appear to be smoother compared to the ones resulting from plotting the predictions of ANN 2. In a similar fashion, the indices in Table 10 reflect the reduced performance of ANN 3. The degradation from the removal of the middle values of the emissivity coefficient appears to be more severe, with absolute maximum error values in the range of 28.46 \({}^{\circ}\)C to 48.84 \({}^{\circ}\)C. Throughout the six tests, algorithm ANN 3 generates a relative absolute error of 45.60% on average, with a maximum of 57.80% for the values predicted for Wall Assembly 2. The overall performance reduction is reflected by the low average coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) of 0.6723 and 18.37, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wall Assembly** & **AME** & **MAE** & **RAE** & **PDIFF** & **PEP** & **RMSE** & **R\({}^{2}\)** \\ \hline WA1 & 1.80 & 0.56 & 2.33\% & 1.80 & 1.7\% & 0.75 & 0.9993 \\ WA2 & 1.63 & 0.78 & 2.78\% & 1.55 & 1.4\% & 0.88 & 0.9992 \\ WA3 & 3.03 & 1.10 & 4.62\% & 2.55 & 2.2\% & 1.29 & 0.9981 \\ WA4 & 2.48 & 0.83 & 2.60\% & 1.39 & 1.2\% & 1.05 & 0.9991 \\ WA5 & 1.69 & 0.45 & 1.42\% & \(-\)0.65 & \(-\)0.5\% & 0.55 & 0.9998 \\ WA6 & 1.67 & 0.64 & 1.97\% & 0.70 & 0.6\% & 0.78 & 0.9995 \\ Average & 2.05 & 0.73 & 2.62\% & 1.22 & 1.1\% & 0.88 & 0.9992 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metric scores for ANN 1 in the testing phase. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wall Assembly** & **AME** & **MAE** & **RAE** & **PDIFF** & **PEP** & **RMSE** & **R\({}^{2}\)** \\ \hline WA1 & 8.45 & 2.69 & 11.3\% & 6.76 & 6.27\% & 3.45 & 0.9844 \\ WA2 & 11.44 & 3.85 & 13.7\% & 2.83 & 2.51\% & 5.07 & 0.9741 \\ WA3 & 32.04 & 5.06 & 21.3\% & 2.34 & 2.03\% & 9.74 & 0.8921 \\ WA4 &  
The most data-deprived network, ANN 4, presents an irregular graphic form of prediction results. In all tests, there is an approximate agreement between predictions and observed values in the lower range of temperatures. However, when the impact of heat transfer becomes more evident on the finite element analysis model (i.e., ground truth values), ANN 4 fails to react accordingly. The indices describing the performance of ANN 4 are presented in Table 11. The average coefficient of determination for ANN 4 between the six tests is 0.8321 and the network's average maximum error is 26.20 \({}^{\circ}\)C. ANN 4 generates ultimate temperature predictions that have an error of 14.55 \({}^{\circ}\)C to 31.65 \({}^{\circ}\)C (12.92% and 24.06% deviation from the actual ultimate temperature). Between the six wall assembly tests, the ANN 4 predictions constitute a mean absolute error of 10.04 \({}^{\circ}\)C (35.44% relative absolute error).