# 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} \hline \hline \multirow{2}{*}{**Configuration**} & \multicolumn{2}{c}{**No. of Units in**} & \multirow{2}{*}{**Dropout**} & \multirow{2}{*}{**Activation**} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{**Dense**} \\  & **the Hidden Layer** & & & & \\ \hline
1 & 32-16 & (0.5.0.5) & ’relu’ & [PERSON] & n\_lags\_out \\
2 & 64-32 & (0.5.0.5) & ’relu’ & [PERSON] & n\_lags\_out \\
3 & 128-64-64 & (0.5.0.2) & ’relu’ & [PERSON] & n\_lags\_out \\
4 & 128-32 & (0.5.0.5) & ’relu’ & [PERSON] & n\_lags\_out \\
5 & 64-32 & (0.5.0.2) & ’relu’ & [PERSON] & n\_lags\_out \\
6 & 128-64 & (0.5.0.2) & ’relu’ & [PERSON] & n\_lags\_out \\
7 & 128-64 & (0.2.0.2) & ’relu’ & [PERSON] & n\_lags\_out \\
8 & 256-128 & (0.2.0.2) & ’relu’ & [PERSON] & n\_lags\_out \\
9 & 256-128 & (0.4.0.4) & ’relu’ & [PERSON] & n\_lags\_out \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Employed configurations of LSTM networks. The main differences between runs occurred in the number of epochs, the batch size, and the number of years captured for training. Actually, all the runs employed the LSTM configuration. In summary, the best model according to the metrics MAE and MSE was produced in run 23, where the number of epochs was set to the value 300, the batch size to 64, and only data acquired in one year were set for training the LSTM network. The following conclusions are evident by analyzing the results in Table 12: (1) the higher the number of years for learning data, the worse the prediction; (2) the higher values of batch sizes (e.g., 250, 128) did not necessarily lead to a better prediction; and (3) although the hyper-parameter 'Batch size' = 500 was also employed during the experimental work, the results were not improved significantly compared with the runs using the lower values of this hyper-parameter. For Generation 2 of the learned models (Table 10), the focus was on forward prediction based on visible past and future data, where the results of the metrics MAE = 0.0390 and MSE = 0.5052 improved significantly compared to the values achieved in the previous generations, while, at the same time, the time complexity was increased before the model converged. Let us mention that only one configuration was tested in Generation 2. As can be concluded from the results in the table, these were improved mainly at the expense of increasing the hyper-parameter 'SW size' to the value of 'SW size' = 24. Thus, the hyper-parameters 'Epochs', 'Batch size', 'Architecture', and 'Years for testing phase' were fixed to the already-found optimal values. The best results in Generation 2 are presented in bold in Table 10.  
Generation 3 (Table 11) was devoted to highlighting the dependence between different hyper-parameters of the LSTM networks. In line with this, the number of epochs was set to higher values, i.e., 'Epochs' = [500,1000], while the batch size was mainly fixed to 64, which was found to be the optimal value in the previous experiments. This generation is distinguished from the others due to comparing different LSTM configurations. The hyper-parameters and 'Years' were fixed to the values 1 year and 12 years, respectively. The best results for Generation 3 are presented in bold in Table 11. \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** & 500/204 & 64 & 1 & 1 & 24 & **0.0390** & **0.0029** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model performance in Generation 2.