# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Searching for the Optimal Air Temperature Prediction Model
There are four predictions presented capturing data from 3.1.2024 till 9.1.2024 in 12-h periods of time. As can be seen from the table, the MAE metric shows that the best learned model had worse results than the MAE metric obtained by the ICON-D2 model, while the comparison according to the Pearson coefficients shows the opposite, i.e., the air temperature time-series of the proposed predicted model matched the observed time-series more than of the ICON-D2 model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Start**} & \multicolumn{2}{c}{**End**} & \multicolumn{2}{c}{**T**} & \multicolumn{2}{c}{**MAE**} & \multirow{2}{*}{**\(\boldsymbol{r_{\text{ICON-D2}}}\)**} & \multirow{2}{*}{**\(\boldsymbol{\boldsymbol{\text{T}_{\text{ICON-D2}}}}\)**} \\
**Date** & **Time** & **Date** & **Time** & **[h]** & & **Maximus** & **ICON-D2** & \\ \hline
03.1.2024 & 19:00 & 04.1.2024 & 06:00 & 12 & 0.9 & 1.1 & 0.6282 & 0.1343 \\
04.1.2024 & 10:00 & 04.1.2024 & 21:00 & 12 & 3.9 & 1.9 & 0.8325 & 0.9943 \\
08.1.2024 & 9:00 & 08.1.2024 & 20:00 & 12 & 2.0 & 0.5 & \(-\)0.4049 & \(-\)0.6946 \\
09.1.2024 & 11:00 & 09.1.2024 & 22:00 & 12 & 0.4 & 0.8 & 0.6592 & 0.6188 \\ n/a & n/a & n/a & n/a & 12 & 1.23 & 1.15 & 0.42875 & 0.2632 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Overview of the metrics for the predictive model of Generation 1 obtained in run 23. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **Epochs** & **Batch** & **Conf.** & **Years** & **SW Size** & **MAE** & **MSE** \\ \hline
1 & 500/75 & 64 & 2 & 1 & 12 & 0.3191 & 0.1846 \\
2 & 500/76 & 32 & 2 & 1 & 12 & 0.3008 & 0.1669 \\
3 & 500/123 & 32 & 2 & 1 & 24 & 0.2907 & 0.1706 \\
**4** & 500/500 & 32 & 2 & 1 & 24 & **0.2815** & **0.1609** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Model performance in Generation 4. Table 14 illustrates the results of the comparative study between the Maximus proposed and ICON-D2 reference prediction models, where the proposed model of Generation 2 obtained in run 1 was taken into closer consideration. Obviously, the optimal proposed model in Table 10 was employed in the study. As is evident from the table, the four weather predictions were illustrated with data captured from 8.1.2024 to 11.1.2024, while the results according to the MAE metric as obtained with the ICON-D2 reference model are slightly worse than those obtained by the best prediction model Maximus. The finding is also justified by the Pearson correlation coefficients, where the \(r_{\text{Maximus}}=0.8019\) is slightly higher than the \(r_{\text{ICON-D2}}=0.8019\), although both indicate a large positive relationship between the observed and predicted air temperature time-series. Finally, the results of the comparative study found by the best prediction model Maximus of Generation 4 obtained in run 4 (Table 12) and the ICON-D2 reference model showed similar results to those in the first experiment, i.e., the MAE metric obtained by the Maximus optimal proposed model was slightly worse that obtained by the ICON-D2 reference model, but the Pearson correlation coefficients showed the opposite, i.e., \(r_{\text{Maximus}}>r_{\text{ICON-D2}}\). As the forecast models were improved, a specific weather situation was chosen for testing. On the selected day, 19.1.2024, it snowed in the morning, clouded up in the afternoon and cleared up gradually in the evening. The comparison between the forecast model and the reference model is presented in Figure 9, which depicts a graph with three lines as follows: (1) the blue line shows the forecast of the reference model, (2) the red line shows the real measurements of the meteorological station, and (3) the green line shows the results of the forecast model.