# 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} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**Generation**} \\  & **1** & **2** & **3** & **4** \\ \hline Input layer & 72 & 108 & 132 & 288 \\ Output layer & 12 & 12 & 12 & 12 \\ Total & 84 & 120 & 144 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of observations at input/output layers. Finally, searching for the best configuration of the LSTM network was established by varying the hyper-parameters 'Number of nodes and hidden layers', 'Dropout', 'Activation', 'Optimizer', and 'Dense'. In line with this, nine different configurations were defined. Actually, the parameter 'Dropout' has a crucial influence on the architecture of the LSTM \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nr.** & **Parameter** & \multicolumn{3}{c}{**Generation**} \\  & **1** & **2** & **3** & **4** \\ \hline
1 & Number of LR & 2 & 1 & 6 & 1 \\
2 & Number of nodes and hidden layers & 1 & 1 & 9 & 1 \\
3 & Number of years for test data & 3 & 1 & 1 & 1 \\
4 & Number of batch sizes & 4 & 1 & 3 & 2 \\
5 & Number of captured years for the testing phase & 3 & 1 & 1 & 1 \\
6 & Number of packets in the sliding window & 1 & 1 & 1 & 2 \\ \(\Sigma\) & Number of configurations & 72 & 1 & 162 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of runs by different hyper-parameter settings of the LSTM networks by generation. Figure 8: Topologies of the LSTM networks applied for the prediction of air temperature according to generations. network due to helping to avoid overfitting in training. The other hyper-parameters influencing the behavior of the LSTM network remained more or less fixed during the tuning process (Table 8). As can be observed from Table 8, the LSTM employed configurations are designated with the numbers 1 to 9, and they are referenced accordingly in the remainder of the paper. The hyper-parameter 'Dropout' refers to the specific hidden layer, while its default value was normally set to the value 0.5. The activation function 'ReLU' was used in our experimental work, which allows the LSTM network to learn nonlinear dependencies. Adam is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure, to update LSTM network weights iteratively based in training data. The neurons of the dense layer are connected to every neuron of its preceding layer. In our case, the number of the dense layer is equal to the number of nodes at the output layer (i.e., n_lags_out = 12). The results obtained in different generations are discussed in detail in the remainder of the section. Let us mention that only the more prominent runs are presented in Tables 9-11. Actually, Generation 1 of the documented learned models (Table 9) was characterized by having the same NN configuration. In total, 13 different runs and 72 more interesting runs are aggregated into the table, which is divided into columns as follows: The column 'Run' denotes the sequence number of a performed LSTM execution for the particular NN architecture. The runs are distinguished between each other with regard to the number of epochs (column 'Epochs'), batch sizes (column 'Batch'), adaptation algorithm (column 'Optimizer'), and number of packets within the SW (column 'SW size'). Thus, the hyper-parameters, like the architecture of the LSTM network (column 'Architecture'), and number of years of test data (column 'Years') were fixed during the experiments. Let us emphasize that the column 'Epochs' reports two values, where the first one designates the value of the hyper-parameter Epoch, while the second value is the real number of epochs before the LSTM networks stopped due to overfitting. The last two columns, 'MAE' and 'MSE', represent the results obtained according to the measures MAE and MSE, respectively. Let us mention that the same structure was applied for the tables representing the results of the other generations as well. The best results in the Generation 1 are presented in bold in Table 9.