## 3 Results and Discussions
\begin{table}
\begin{tabular}{|c|c c|c c|c c|c c|c c|c c|} \hline  & \multicolumn{3}{c|}{**LSTM**} & \multicolumn{3}{c|}{**CNN+LSTM**} & \multicolumn{3}{c|}{**DNN**} & \multicolumn{3}{c|}{**Bagging Ensemble Model**} \\ \cline{2-13}  & **MAPE (\%)** & **MAE (MW)** & **MSE (GW\({}^{3}\))** & **MAPE (\%)** & **MAE (MW)** & **MSE (GW\({}^{3}\))** & **MAPE (\%)** & **MAE (MW)** & **MSE (GW\({}^{3}\))** & **MAPE (\%)** & **MAE (MW)** & **MSE (GW\({}^{3}\))** \\ \hline Jan & 13.08 & 2235.17 & 9070.42 & 12.88 & 2185.09 & 8397.09 & 13.72 & 2359.98 & 9774.87 & 12.98 & 2093.38 & 8903.46 \\ Feb & 4.84 & 1023.00 & 1406.87 & 4.73 & 1013.17 & 1579.03 & 4.76 & 1014.28 & 1456.72 & 3.29 & 680.38 & 813.99 \\ Mar & 5.97 & 1445.93 & 2734.75 & 6.38 & 1594.10 & 3330.44 & 6.09 & 1472.09 & 2827.14 & 6.48 & 1569.43 & 2960.72 \\ Apr & 12.68 & 2626.65 & 11.081.15 & 13.12 & 2746.25 & 11.586.12 & 12.71 & 2663.27 & 1132.87 & 9.94 & 2130.98 & 7068.72 \\ May & 7.46 & 1747.65 & 4822.34 & 7.14 & 1664.28 & 4675.03 & 7.34 & 1728.00 & 4822.21 & 7.33 & 1756.00 & 4480.45 \\ Jun & 4.92 & 1714.47 & 2056.87 & 5.14 & 1227.47 & 2269.68 & 4.95 & 1181.06 & 2106.70 & 4.88 & 1180.28 & 1970.48 \\ Jul & 5.22 & 1128.93 & 2049.82 & 5.12 & 1111.66 & 1994.53 & 5.08 & 10092.44 & 1980.59 & 4.17 & 907.72 & 1406.96 \\ Aug & 5.35 & 1201.36 & 2214.92 & 5.25 & 1179.41 & 2120.09 & 5.34 & 1197.99 & 2212.39 & 4.17 & 928.65 & 1246.86 \\ Sep & 3.43 & 755.24 & 930.58 & 4.05 & 893.08 & 1239.53 & 3.40 & 748.40 & 905.92 & 2.36 & 512.57 & 421.70 \\ Oct & 4.47 & 977.75 & 1439.40 & 4.54 & 996.56 & 1455.05 & 4.59 & 1004.31 & 1503.78 & 3.16 & 681.71 & 754.51 \\ Nov & 4.74 & 1040.95 & 1422.87 & 4.92 & 1082.02 & 1569.09 & 4.85 & 1065.46 & 1478.61 & 3.88 & 844.52 & 928.57 \\ Dec & 8.49 & 1534.56 & 5032.24 & 8.51 & 1545.56 & 5016.54 & 8.39 & 1518.97 & 4857.91 & 9.62 & 1754.26 & 5920.08 \\ \hline Average & 6.74 & 1413.74 & 3712.15 & 6.85 & 1439.48 & 3783.01 & 6.79 & 1424.50 & 3772.21 & 6.05 & 1258.98 & 3099.11 \\ \hline \end{tabular}
\end{table} 
Table 2: Error Measurements of Test Data for Five Input Features in 2021. Monthly error accuracies for all models using nine inputs that have additional dummy variables in terms of calendar effects are measured in Table 4. In this case, the bagging ensemble model outperforms all other discussed training models, showing 5.91% _MAPE_, 1230.39 MW _MAE_, and 2700.84 GW\({}^{2}\)_MSE_. Regarding the average accuracy, the DNN model attains 6.77% _MAPE_, 1421.41 MW _MAPE_, and 3759.89 GW\({}^{2}\)_MSE_, while the LSTM and CNN+LSTM models observe 0.1% lower MAPE than the DNN model. In this case, adding dummy variables slightly increases the forecasting accuracy of our bagging ensemble model. As illustrated in Table 5, ten inputs, including additional temperature and dummy variables, are trained for all models to measure the accuracy. In this case, our proposed model still provides the minimum error with 6.00% _MAPE_, 1264.31 MW _MAE_, and 2840.87 GW\({}^{2}\)_MSE_. The LSTM and DNN came next with almost similar MAPE, representing around 6.75% _MAPE_, followed by the CNN+LSTM model having 6.87% _MAPE_. According to all four result tables (Tables 2-5), our proposed model has no sensitivity for the selection of input features because of similar resulting errors. However, additional temperature input only causes a higher error in the proposed model. Each forecasting model obtains the minimum error on different input features in the comparative analysis among input features. The LSTM, CNN-LSTM, DNN models correspondingly provide the lowest error at five inputs, six inputs, and ten inputs. All in all, our proposed model executes the best performance in all experiments. Five categories, such as holidays, bridging holidays, Mondays, weekdays, and weekends are grouped for all four forecasting models to check the _MAPE_ results in each category. Table 6 refers to the average _MAPE_ of 2021 test predictions for all models according to different input structures.