## 6 Results and Discussion
### RNN-GRU Performance: Scenario1  
For _Scenario_1, the \(RNN\)-\(GRU\) model underperformed compared to the \(FNN\) and \(RNN\)-\(LSTM\) models. Through our \(RNN\)-\(GRU\) experimental setup, we achieved a minimum validation loss of 200.75 MWatt, which was the highest MAE against 170.41 MWatt and 195.31 MWatt. This outcome was obtained by selecting a single-layered network with 32 nodes, a look-back period of 5 days, and training the model for 51 epochs. By utilizing the optimized parameters mentioned earlier with a dataset size of 44,256 and a test size of 11,520, we conducted predictions for the electricity demand in 2013. The resulting Mean Absolute Percentage Error (MAPE) was found to be 3.37% for _scenario_1. It should be noted that this data setup specifically excluded holidays and weekends, focusing solely on weekdays. The visualization of the prediction results is presented in the accompanying Figure 17, where the actual demand is denoted as _load_ and forecasting results are denoted as _load_pred_. The the \(RNN\)-based \(GRU\) and \(LSTM\) models underperformed compared to \(FNN\) in _Scenario_1; the corresponding MAPEs are as shown below. According to Table 6 (and see details in Appendix A, Table A1), the minimum loss of 170.41 MW is achieved when the parameters are set as follows: nnodes = 32, nlayers = 2, lookback = 10 days, and dropout = 0 over the course of 351 epochs. We tested for various trial and error cases with the variables, finding that excluding the month variable did not lead to a significant improvement in MAPE when using the FNN model. However, incorporating the maximum temperature variable in the LSTM model resulted in an improvement of 1.39 MW in MAE, and the MAPE values decreased to 2.54% (Figure 18). This indicates that including the maximum temperature variable enhanced the model's predictive accuracy. Next, we considered the entire dataset in _Scenario_2, including demand values for holidays, weekends, and weekdays. Additional hyperparameter tuning experiments were conducted, with the results shown in Table 5. We achieved the following results in _Scenario_2:  
* For the FNN model, the minimum validation loss of 237.82 MWatt was obtained when nnodes = 64, nlayers = 3, lookback = 8 days, dropout = 0, and epochs = 180. * For the GRU model, the minimum validation loss of 234.22 MWatt occurred when nnodes = 64, nlayers = 2, lookback = 8 days, dropout = 0, and epoch = 99. * Similarly, for the LSTM model, the minimum validation loss of 242.12 MWatt was achieved when nnodes = 64, nlayers = 2, lookback = 8 days, dropout = 0, and epoch = 56. The MAE values obtained from the optimized parameters indicate that the RNN-GRU model outperformed the other models. The overall results are summarized below. Table 7 reflects the superior performance of the RNN-GRU model in terms of both MAE and MAPE. We tabulated the prediction results obtained by the FNN, LSTM, and GRU networks trained by using the aggregate data and testing for 2013, with the results shown below. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **nnodes/Layer nlayers** & \begin{tabular}{c} **Look** \\ **Back** \\ \end{tabular} & **dropout** & **epoch** & \begin{tabular}{c} **Min** \\ **MAE** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **MAE** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **MAPE(\%)** \\ \end{tabular} \\ \hline FNN & 32 & 2 & 10 days & 0 & 351 & 170.41 & 165.54 & 2.47 \\ GRU & 32 & 2 & 5 days & 0 & 51 & 200.75 & 192.76 & 3.37 \\ LSTM & 32 & 1 & 5 days & 0 & 69 & 195.31 & 179.83 & 2.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Implementation of the best parameters for day-ahead forecasting (_Scenario_1). Table 8 indicates clearly that the RNN-based GRU model has the best accuracy for _Scenario2_, i.e., the aggregated dataset. The overall MAPE is increased due to the volatile nature of demand during weekends and holidays, as discussed in Section 4.