## 3 Results
### Experiment 2  
To check how the network size can affect the results when changing the temporal aggregation, the same experiment of Section 3.1 is repeated, but now the number of nodes of the SLFN are changed to 30, (Table 5), 50 (Table 6), and 60 (Table 7). Likewise, in Figure 8 the RMSE obtained by a network of \(H\) hidden nodes for the range \(3\leq H\leq 90\) and for each one of the WTs is depicted, when the models are trained with 5-min data and 60-min data. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{4}{c}{RMSE} \\ \hline  & **WT\_id** & **5 min** & **10 min** & **15 min** & **30 min** & **60 min** \\ \hline \multirow{4}{*}{**Training**} & 80 & 0.9419 & 0.8835 & 0.9013 & 0.8622 & **0.8548** \\  & 81 & 0.9641 & 0.9218 & 0.9694 & 0.8622 & **0.9096** \\  & 82 & 1.9675 & 1.9386 & 2.0395 & 1.9727 & **1.8542** \\  & 83 & 0.9792 & 0.9999 & 0.9848 & 0.9328 & **0.9098** \\ \hline \multirow{4}{*}{**Test**} & 80 & 1.2313 & 1.1700 & 1.1733 & 1.2094 & **1.1414** \\  & 81 & 3.3673 & 3.2796 & 3.2759 & 3.0293 & **2.7340** \\ \cline{1-1}  & 82 & 2.4562 & 2.3924 & 2.3594 & 2.3460 & **1.9705** \\ \cline{1-1}  & 83 & 2.3632 & 2.3699 & 2.2608 & 2.1164 & **1.8810** \\ \hline \end{tabular}
\end{table} 
Table 4: Results obtained averaging the RMSE of 25 models both in the training and test steps when trained with data of different temporal scales. In all cases, the trained network a SLFN with 40 hidden nodes. The best values for all the WTs (highlighted in bold), are obtained when using 60-min aggregation. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{4}{c}{RMSE} \\ \hline  & **WT\_id** & **5 min** & **10 min** & **15 min** & **30 min** & **60 min** \\ \hline \multirow{4}{*}{**Training**} & 80 & 1.1329 & 1.1540 & 1.1616 & 1.0955 & **1.0876** \\  & 81 & 1.1369 & 1.1301 & 1.1522 & 1.1183 & **1.1157** \\  & 82 & 2.1591 & 2.2547 & 2.2765 & 2.2210 & **2.1099** \\  & 83 & 1.1560 & 1.1387 & 1.1599 & **1.0820** & 1.1101 \\ \hline \multirow{4}{*}{**Test**} & 80 & 1.4261 & 1.4087 & 1.4689 & 1.3606 & **1.3408** \\  & 81 & 3.4268 & 3.3469 & 3.3642 & 3.1364 & **2.7909** \\ \cline{1-1}  & 82 & 2.5473 & 2.4813 & 2.4294 & 2.3299 & **2.0261** \\ \cline{1-1}  & 83 & 2.4484 & 2.4414 & 2.3644 & 2.1850 & **2.0420** \\ \hline \end{tabular}
\end{table} 
Table 5: Results obtained by averaging the RMSE of 25 models both in the training and test steps when trained with data of different temporal aggregation. In all cases, the trained network is a SLFN with 30 hidden nodes. The minimum values, highlighted in bold, are obtained when 60-min temporal aggregation is used in all WTs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{RMSE} \\ \hline  & **WT\_id** & **5 min** & **10 min** & **15 min** & **30 min** & **60 min** \\ \hline \multirow{5}{*}{**Training**} & 80 & 0.8279 & 0.8294 & 0.7929 & **0.7590** & 0.7788 \\  & 81 & 0.7896 & 0.8501 & 0.8009 & 0.7809 & **0.7495** \\  & 82 & 1.7718 & 1.7437 & 1.8209 & 1.7915 & **1.7439** \\  & 83 & 0.8792 & 0.8899 & 0.8233 & 0.8402 & **0.8165** \\ \hline \multirow{5}{*}{**Test**} & 80 & 1.1227 & 1.1235 & 1.0766 & **1.0123** & 1.0572 \\  & 81 & 3.3120 & 3.2281 & 3.1987 & 2.9994 & **2.6630** \\ \cline{1-1}  & 82 & 2.4299 & 2.3658 & 2.3636 & 2.2869 & **1.9865** \\ \cline{1-1}  & 83 & 2.3006 & 2.3108 & 2.1712 & 2.0670 & **1.8304** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained by averaging the RMSE of 25 models both in the training and test steps when trained with data of different temporal aggregation. In all cases, the trained network is a SLFN with 50 hidden nodes. The minimum values, highlighted in bold, are obtained when 60-min temporal aggregation is used in all WTs.