# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Searching for the Optimal Air Temperature Prediction Model  
In the first experiment, the Maximus optimal model for the prediction of air temperature was searched for. At first, the best LSTM topology was indicated by the developer; then, the optimal hyper-parameters were determined, and, finally, the proposed prediction model was evaluated according to the MAE metric. The more appropriate LSTM topologies were determined in four generations, where sequences of past data for different sets of features and different SW sizes per feature were captured at the input layer on an hourly basis (Figure 8). Thus, the output layer is unique in all cases, and consists of \(T\) future values. No data was found on the satellite source of the weather model. It is incorporated into both \begin{table}
\begin{tabular}{c c} \hline \hline
**Measuring Range** & **Description** \\ \hline Type & LENOVO IdeaPad L340-15 FHD \\ CPU & Intel Core I5-9300H \\ RAM & 8 GB \\ GPU & GeForce GTX 1050 \\ SW & Python 3.10.13 libraries math, sklearn \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hardware and software characteristics of the computer, on which experiments were run. models through data assimilation beforehand, so we obtained prepared data for that. So, in situ observations and satellites provide weather data with data assimilation techniques. As can be seen in Figure 8, the generations differ between each other in the number of features, as found in the lower-resolution CERRA dataset placed in the input layer as the past data. Let us mention that there are a lot of feature variables divided into different categories [31], where only a specific set of these were selected for the particular generation. The presence of the definite feature entered at the LSTM input in a specific generation is denoted with a green circle in the table. For instance, six various features were employed in Generation 1, while even 21 features determine the predicted air temperature in Generation 4. Depending on the generation, the number of inputs from the RNWP model ICON-D2 varies from generation to generation. The more reliable inputs are therefore expected in Generation 1, where only one input is taken from the predicted RNWP model ICON-D2, while the remainder of the inputs are pure real values. On the other hand, the feature 'Air temperature at 2 m' arises as the future data in all the observed generations. This fact is denoted with the corresponding violet squares in the table. Interestingly, each feature emerging at the input layer enters as a time sequence consisting of observations (also packets) captured on an hourly basis. Actually, the number of observations is determined by the SW size per feature. Let us assume that we support \(T=12\) observations per day. Then, the number of observations at the input/output layers per generation is that presented in Table 6. The total number of observations is added to the last row in the table. In summary, the total number of observations in Generation 1 is 72, because, for each of the six features, we have \(T=12\) observations per feature, which amounts to \(6\times 12=72\) in total. In summary, the number of features increases by increasing the number of generations. Tuning the hyper-parameters of the LSTM network was conducted after the scenarios illustrated in Table 7. As is evident from the table, the optimal values of six hyper-parameters were searched for during the experimental work. These hyper-parameters were tuned by modifying the particular parameter in the number of different runs. For instance, the hyper-parameter 'Number of nodes and hidden layers' was tuned in one run per generation, except for Generation 3, where the hyper-parameter was modified in nine different configurations (i.e., the settings of the LSTM hyper-parameters). Interestingly, in the last row of the table, the total number of runs is presented, from which it can be observed that 239 different runs of different LSTM architectures were employed in the summary.