## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Neurons** & **Neurons** & **Dense** & **Lookbacks** & **RMSE (Joule)** & **MAE (Joule)** & **MSE (Joule)** & **MAPE (\%)** \\ \hline
**5** & **5** & **10** & **5** & **47.824** & **37.945** & **0.004** & **0.4** \\
10 & 10 & 10 & 5 & 53.587 & 42.816 & 0.004 & 0.4 \\
15 & 15 & 10 & 5 & 60.238 & 51.231 & 0.005 & 0.5 \\
20 & 20 & 10 & 5 & 95.778 & 87.149 & 0.008 & 0.8 \\
25 & 25 & 10 & 5 & 83.127 & 75.284 & 0.007 & 0.7 \\
30 & 30 & 10 & 5 & 64.154 & 52.420 & 0.005 & 0.5 \\
35 & **35** & **10** & **5** & **42.650** & **33.574** & **0.003** & **0.3** \\
40 & 40 & 10 & 5 & 72.566 & 57.842 & 0.006 & 0.6 \\
45 & 45 & 10 & 5 & 64.616 & 54.205 & 0.005 & 0.5 \\
50 & 50 & 10 & 5 & 60.420 & 49.234 & 0.005 & 0.5 \\
55 & 55 & 10 & 5 & 67.376 & 55.351 & 0.005 & 0.5 \\
60 & 60 & 10 & 5 & 53.054 & 42.452 & 0.004 & 0.4 \\
65 & 65 & 10 & 5 & 47.793 & 38.947 & 0.004 & 0.4 \\
70 & 70 & 10 & 5 & 68.445 & 54.603 & 0.005 & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 9: The result of LSTM testing. In Figure 19, a graph or visualization of the loss values computed during the model training process is presented. These results measure how much the predicted values differ from the actual values. In Figure 19a, the loss results for Model 1 are shown, utilizing the CNN-LSTM method, where each CNN and LSTM layer has 10 neurons, resulting in an RMSE of 38.052. Meanwhile, in Figure 19b, the loss results for Model 2 using the CNN-LSTM method are displayed, with each CNN and LSTM layer having 50 neurons and an RMSE of 32.436, as indicated in Table 11. Each model in the training process utilized 240 epochs and the observed spikes in the early stages of the training loss. This \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Neurons** & **Neurons** & **Neurons** & **Lookbacks** & **RMSE (Joule)** & **MAE (Joule)** & **MSE (Joule)** & **MAPE (\%)** \\ \hline
5 & 5 & 10 & 5 & 85.404 & 67.058 & 0.007 & 0.7 \\
10 & 10 & 10 & 5 & 41.459 & 33.377 & 0.003 & 0.3 \\
15 & 15 & 10 & 5 & 38.380 & 30.588 & 0.003 & 0.3 \\
20 & 20 & 10 & 5 & 45.492 & 36.244 & 0.004 & 0.4 \\
25 & 25 & 10 & 5 & 41.660 & 33.074 & 0.003 & 0.3 \\
**30** & **30** & **10** & **5** & **37.873** & **30.209** & **0.003** & **0.3** \\
35 & 35 & 10 & 5 & 47.039 & 37.867 & 0.004 & 0.4 \\
40 & 40 & 10 & 5 & 59.837 & 48.592 & 0.005 & 0.5 \\
45 & 45 & 10 & 5 & 61.525 & 51.413 & 0.005 & 0.5 \\
50 & 50 & 10 & 5 & 45.261 & 35.964 & 0.003 & 0.3 \\
**55** & **55** & **10** & **5** & **37.675** & **30.082** & **0.003** & **0.3** \\
60 & 60 & 10 & 5 & 70.768 & 62.246 & 0.006 & 0.6 \\
65 & 65 & 10 & 5 & 46.593 & 37.493 & 0.004 & 0.4 \\
70 & 70 & 10 & 5 & 50.010 & 40.676 & 0.004 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The result of CNN testing. Figure 17: Results of an energy prediction model with CNN: (a) 30 neurons-1 and 30 neurons-2; (b) 55 neurons-1 and 55 neurons-2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Neurons** & **Neurons** & **Neurons** & **Lookbacks** & **RMSE (Joule)** & **MAE (Joule)** & **MSE (Joule)** & **MAPE (\%)** \\ \hline
5 & 5 & 5 & 5 & 5 & 53.678 & 43.037 & 0.004 & 0.4 \\
**10** & **10** & **10** & **10** & **5** & **38.052** & **30.266** & **0.003** & **0.3** \\
15 & 15 & 15 & 15 & 5 & 53.819 & 43.775 & 0.004 &  
phenomenon can be attributed to the occurrence of vanishing gradients, particularly when the ReLU activation function is employed during the training process. In this section, we also compare the results of the energy consumption prediction model with similar methods. We have compared the results of several methods, namely elastic net (ELNET) regression, support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), ridge regression (RR), and kernel ridge regression (KRR).