# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Searching for the Optimal Air Temperature Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **Epochs** & **Batch** & **Conf.** & **Years** & **SW Size** & **MAE** & **MSE** \\ \hline
13 & 30/30 & 32 & 1 & 5 & 12 & 1.2205 & 2.3841 \\
14 & 300/34 & 32 & 1 & 5 & 12 & 1.2327 & 2.3185 \\
15 & 300/52 & 32 & 1 & 1 & 12 & 1.1232 & 1.9552 \\
16 & 300/41 & 64 & 1 & 1 & 12 & 1.0912 & 2.0472 \\
17 & 300/38 & 64 & 1 & 1 & 12 & 0.9619 & 1.6091 \\
18 & 300/60 & 128 & 1 & 1 & 12 & 0.6694 & 0.8425 \\
19 & 300/96 & 128 & 1 & 1 & 12 & 0.6575 & 0.7791 \\
20 & 300/124 & 128 & 1 & 1 & 12 & 0.5933 & 0.6624 \\
21 & 300/79 & 256 & 1 & 7 & 12 & 0.5272 & 0.5349 \\
22 & 300/56 & 64 & 1 & 7 & 12 & 0.9258 & 1.5472 \\
**23** & 300/186 & 64 & 1 & 1 & 12 & **0.4902** & **0.4945** \\
24 & 500/73 & 128 & 1 & 1 & 12 & 1.1418 & 2.2701 \\
25 & 500/202 & 64 & 1 & 1 & 12 & 0.4958 & 0.5052 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Model performance in Generation 1. As is evident from the table, the best model was found in run 1, where the metrics MAE = 0.4873 and MSE = 0.4742 were achieved by employing the LSTM of Configuration 1. Interestingly, significant deviations can be observed in the _MAE_ and _MSE_ metrics designated by values in the red color (i.e., runs 5, 10, 14). This is because, here, a lot of experimentation was devoted to modifying the internal metrics, such as the LR and the architecture of the neural network, by removing and adding different layers, as well as neurons. Typically, these models suffered from an overfitting that arose when the model contained more parameters than could be justified by the data. In Generation 4 of the learned model settings (Table 12), some of the best models are presented that reached this level with certain settings. Typically, these models were obtained by using the LSTM Configuration 2 and a batch size of 32. Interestingly, the best results according to the metrics MAE = 0.2815 and MSE = 0.1609 were obtained in run 4, where the number of epochs was set to the value 500, the number of years for testing set to 1, and the SW size to 24. Interestingly, these results were obtained by maximizing the real number of epochs to 500. The best results for Generation 4 are presented in bold in Table 12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Run** & **Epochs** & **Batch** & **Conf.** & **Years** & **SW Size** & **MAE** & **MSE** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 11: Model performance in Generation 3. Let us emphasize that the best results for this particular generation are best for the specific values due to the same configuration hyper-parameters. In general, the best results are hard to find because of variable weather conditions, i.e., each weather situation has another starting condition that strongly influences our model's prediction. The variances are substantial within the exact prediction, which can happen in one hour. Comparison of the Maximus Optimal Air Temperature Prediction Model with the Reference Model ICON-D2 Dataset  
The aim of the second experiment was to verify the Maximus optimal models as found during the first test in a real environment. Tables 13-15 show the MAE metrics of a particular version denoted as the Maximus model, which are compared with the MAE of the reference ICON-D2 model. Additionally, the results of both models are compared according to the Pearson correlation coefficient, indicating a relationship between the observed and predicted time-series. The Pearson coefficient values are drawn from the interval \(r\in[-1,1]\), where \(r\geq 0.5\) indicates a large positive relationship between the observed and predicted time-series: \(r\in[0.3,0.5)\) medium, \(r\in[0.1,0.3)\) small, and \(r<0.1\) very small. Indeed, three models with different LSTM architectures were observed in the comparative study. Table 13 aggregates the results of the comparative study between the Maximus optimal and the ICON-D2 reference model. Thus, the prediction model of Generation 1 obtained in run 23 (i.e., the best model in Table 9) was taken into consideration.