## 5 Result and Discussion
All models were run for 20, 35, and 50 epochs; then, our proposed model, in which the results of prediction are represented by the orange line, came closest to the actual value, which is represented by the blue line. Notably, in Figure A2e, the line of validation loss got a spike in epoch 15 th, then it continued to have the same trend with train loss until epoch 50 th. When the number of epochs is increased from 20 to 35, and then 50 epochs, the MSE, MAE, and MAPE values from the proposed model tend to decrease to the lowest error compared to other methods. These results demonstrate that Multi-Dense Layer BiLSTM has the best fit pattern than the other four models. The authors took a sample to combine the fitting trends from all models for one hour ahead prediction, with each model run by 20 epochs, which are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & **Prediction** & \multicolumn{4}{c}{**MSE**} & \multicolumn{4}{c}{**MAE**} & \multicolumn{4}{c}{**MAPE**} \\ \cline{2-10}  & **Time** & **20** & **35** & **50** & **20** & **35** & **50** & **20** & **35** & **50** \\  & **Length** & **Epoch** & **Epoch** & **Epoch** & **Epoch** & **Epoch** & **Epoch** & **Epoch** & **Epoch** \\ \hline \multirow{3}{*}{RNN} & 1 h & 0.1072 & 0.1141 & 0.1001 & 0.2415 & 0.2371 & 0.2199 & 29.8106 & 31.0264 & 27.7334 \\  & 2 h & 0.1012 & 0.1138 & 0.1165 & 0.1778 & 0.1986 & 0.2097 & 23.89 & 27.4217 & 29.8114 \\  & 3 h & 0.0833 & 0.0908 & 0.072 & 0.2501 & 0.2337 & 0.2132 & 54.1121 & 56.2735 & 52.1554 \\ \hline \multirow{3}{*}{LSTM} & 1 h & 0.0058 & 0.0048 & 0.0045 & 0.0626 & 0.0548 & 0.055 & 11.4597 & 8.8899 & 9.1937 \\  & 2 h & 0.0187 & 0.0771 & 0.0217 & 0.125 & 0.1985 & 0.1325 & 25.617 & 68.8899 & 27.6512 \\  & 3 h & 0.0619 & 0.0724 & 0.066 & 0.1703 & 0.2023 & 0.1765 & 60.5662 & 67.6347 & 63.2913 \\ \hline \multirow{3}{*}{CNN-LSTM} & 1 h & 5.838 & 3.907 & 3.69 & 2.305 & 1.706 & 1.676 & 6.061 & 3.613 & 4.332 \\  & 2 h & 3.871 & 3.992 & 3.871 & 1.659 & 1.739 & 1.659 & 4.864 & 4.948 & 4.864 \\  & 3 h & 8.434 & 8.434 & 8.434 & 2.598 & 2.598 & 2.598 & 7.64 & 7.64 & 7.64 \\ \hline \multirow{3}{*}{Single-Dense Layer BiLSTM} & 1 h & 0.0016 & 0.0017 & 0.0016 & 0.0029 & 0.0034 & 0.3266 & 0.3385 & 0.3434 & 0.3047 \\  & 2 h & 0.0015 & 0.0027 & 0.0014 & 0.318 & 0.0042 & 0.3105 & 0.3046 & 0.4193 & 0.2814 \\  & 3 h & 0.0053 & 0.0047 & 0.0063 & 0.00 In the zoomed Figure 7, the authors took sample lines from all compared models from 25 to 35 s, indicating that the proposed model represented by the orange line has the closest distance to the real data represented by the blue line. The proposed model and single-dense layer BiLSTM represented by the green line have a close connection. This result shows that the proposed model, which uses multi-dense layers, could empower network stability [28]. From Figure 7, we can also see that the CNN-LSTM model, represented by the red line, has the furthest distance from the real data. This result is suitable to the values mentioned in Table 3. This is understandable given that CNN-LSTM is essentially slower due to its operations and necessitates a lengthy process [36]. Figure 6: Result of loss function using the proposed model for 1 h ahead prediction (**a**,**b**) 20 epochs. (**c**,**d**) 35 epochs. (**e**,**f**) 50 epochs.