## 4 Results and Discussion
First, the proximity of Sensor 1 and Sensor 2 to the heat source results in higher temperatures in their vicinity due to the direct influence of the heat source. The second factor is the bluer fan's lack of even hot air distribution, so there are hot areas in the incubator. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variable** & **T\_1** & **T\_2** & **T\_3** & **T\_4** & **H\_1** & **H\_2** & **H\_3** & **H\_4** & **Ampere** & **Joule** \\ \hline count & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 & 65,535 \\ mean & 34.22 & 39.02 & 31.76 & 31.77 & 34.03 & 34.22 & 43.76 & 36.45 & 0.73 & 161.47 \\ std & 0.48 & 1.14 & 0.27 & 1.02 & 2.62 & 1.51 & 2.47 & 2.60 & 0.09 & 21.63 \\ min & 28.90 & 31.10 & 27.90 & 27.10 & 29.00 & 32.40 & 38.80 & 31.00 & 0.45 & 99.00 \\ max & 35.20 & 45.30 & 32.60 & 33.80 & 46.00 & 46.40 & 51.40 & 45.00 & 0.92 & 202.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical information of the dataset. Figure 13: Data sensor: (**a**) Temperature 1 (T\_1); (**b**) Temperature 2 (T\_2); (**c**) Temperature 3 (T\_3; (**d**) Temperature 4 (T\_4); (**e**) Humidity 1 (H\_1); (**f**) Humidity 2 (H\_2); (**g**) Humidity 3 (H\_3); (**h**) Humidity 4 (H\_4); (**i**) electric current (Ampere); and (**j**) energy (Joule). Next, the dataset undergoes preprocessing in training and testing the model for predicting electricity usage. This involves calculating the total energy consumption (Joules) and computing the average values for the temperature attributes (_T_1_, \(T\)2_, \(T\)3_, \(T\)4_) and humidity (_H_1_, \(H\)2_, \(H\)3_, \(H\)4_) for each minute (60 s). As a result, a new dataset is obtained with energy values ranging from 7.000 to 11.000 Joules, as depicted in Figure 15. Subsequently, the data are preprocessed to scale for the training dataset. Figure 14: Temperature distribution. Figure 15: Target energy attribute data (Joule). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **T\_1** & **H\_1** & **T\_2** & **H\_2** & **T\_3** & **H\_3** & **T\_4** & **H\_4** & **Ampere** & **Joule** \\ \hline T\_1 & \(1\) & \(-0.43\) & \(0.1\) & \(-0.31\) & \(0.69\) & \(-0.27\) & \(-0.034\) & \(-0.22\) & \(-0.2\) & \(-0.2\) \\ H\_1 & \(-0. In this research, the testing of the model is divided into three parts. First, the initial model is created using the LSTM method. Second, the model is created using the CNN method. Third, a model is developed by combining the CNN and LSTM methods. The best RMSE and MAE results for the LSTM method are 42.650 and 33.575, respectively. The LSTM layer consists of 35 neurons, the dense layer contains 10 neurons, and the lookback value is set to 5. The complete testing results are presented in Table 9. The comparison between the predicted and actual values is shown in Figure 16a (with five neurons-1 and five neurons-2) and Figure 16b (with thirty-five neurons-1 and thirty-five neurons-2). In the second test using the CNN method, the best results for RMSE and MAE were 37.675 and 30.082, respectively. The optimal model for this test was achieved using 55 neurons-1 and 55 neurons-2. The comprehensive results can be found in Table 10, and the comparison between the predicted and actual values is illustrated in Figure 17a (with 30 neurons-1 and 30 neurons-2) and Figure 17b (with 55 neurons-1 and 55 neurons-2). In the third test using the combination of CNN and LSTM, the best results were achieved with RMSE values of 32.436 and MAE values of 25.382. Furthermore, compared to the previous methods' results, the combination of CNN and LSTM outperformed them, indicating an improvement in the model's performance using this combination. Detailed results can be found in Table 11, and the comparison between the predicted and actual values using the CNN-LSTM method is illustrated in Figure 18a,b. Figure 16: Results of an energy prediction model with LSTM: (**a**) 5 neurons-1 and 5 neurons-2; (**b**) 35 neurons-1 and 35 neurons-2.