# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**R Table**} \\ \hline t state & t + 1 & \multicolumn{2}{c}{t + 2} & t + 3 & [PERSON] & RMSprop & SGD \\ \hline \multirow{6}{*}{[PERSON]} & \multirow{3}{*}{Adam} & \multirow{3}{*}{0} & [PERSON] & 0 & [PERSON] & 0 & \(-\)53.60 & \(-\)6643.26 \\ \cline{3-8}  & & & RMSprop & \(-\)53.60 & RMSprop & 53.60 & 0 & \(-\)6589.66 \\ \cline{3-8}  & & & SGD & \(-\)6643.26 & SGD & 6643.26 & 6589.66 & 0 \\ \cline{3-8}  & & & Adam & 53.60 & [PERSON] & 0 & \(-\)53.60 & \(-\)6643.26 \\ \cline{3-8}  & & & RMSprop & 0 & RMSprop & 53.60 & 0 & \(-\)6589.66 \\ \cline{3-8}  & & & SGD & \(-\)6589.66 & SGD & 6643.26 & 6589.66 & 0 \\ \cline{3-8}  & & & Adam & 6643.26 & Adam & 0 & \(-\)53.60 & \(-\)6643.26 \\ \cline{3-8}  & & & RMSprop & 6589.66 & RMSprop & 53.60 & 0 & \(-\)6589.66 \\ \cline{3-8}  & & & SGD & 0 & SGD & 6643.26 & 6589.66 & 0 \\ \hline \multicolumn{8}{c}{**Q Table**} \\ \hline \multicolumn{8}{c}{**1 nd iteration**} \\ \hline  & & \multicolumn{2}{c}{t state} & \multicolumn{2}{c}{t + 1 state} \\ \hline  & & \multicolumn{2}{c}{t state} & \multicolumn{2}{c}{t + 1} & \multicolumn{2}{c}{t + 2} \\ \hline  & & \multicolumn{2}{c}{[PERSON]} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{[PERSON]} & 0 \\ \cline{3-8}  & & & Adam & 0 & RMSprop & 0 \\ \cline{3-8}  & & & & SGD & 0 \\ \cline{3-8}  & & & & Adam & 0 \\ \cline{3-8}  & & & & RMSprop & 0 \\ \cline{3-8}  & & & SGD & 0 \\ \hline \multicolumn{8}{c}{**2 st iteration**} \\ \hline  & t state & \multicolumn{2}{c}{t + 1} & \multicolumn{2}{c}{t + 2} \\ \hline  & & \multicolumn{2}{c}{[PERSON]} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{[PERSON]} & 0 \\ \cline{3-8}  & & & Adam & 0 & RMSprop & 0 \\ \cline{3-8}  & & & SGD & 0 \\ \cline{3-8}  & & & SGD & \(-\)4650.28 & RMSprop & 0 \\ \cline{3-8}  & & & SGD & 0 \\ \hline \multicolumn{8}{c}{**3 rd iteration**} \\ \hline t state & t + 1 & \multicolumn{2}{c}{t + 2} & t + 3 & Adam & RMSprop & SGD \\ \hline \multirow{6}{*}{Adam} & \multirow{3}{*}{0} & Adam & 0 & Adam & 0 & 0 & 0 \\ \cline{3-8}  & & & RMSprop & \(-\)37.52 & RMSprop & 0 & 0 & 0 \\ \cline{3-8}  & & & SGD & \(-\)4650.28 & SGD & 0 & 0 & 0 \\ \cline{3-8}  & & & SGD & \(-\)4612.76 & RMSprop & 0 & 0 & 0 \\ \cline{3-8}  & & & SGD & 0 & SGD & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A record of the Q-learning computation process for selection of optimizer. In the experiment, the architecture parameters were determined by computing the Q-table (Figure 6). Finally, the architecture parameters were selected according to the results of the Q-learning computation and are listed in Table 7.  
Then, the selected hyperparameters for the TCN were used to draw comparisons and calculate the predictive precision of the proposed scheme. As shown in Figure 8, we evaluated the accuracy of the model in terms of prediction ability to examine the expected outcome and whether the appropriate model parameters were selected. The optimal hyperparameters for the TCN can be determined through the error value and error reduction speed of the loss function. Specifically, the model parameters may require adjustment if the convergence error decrease is not smooth. From Figure 8, the TCN accuracy for wind power prediction increased with a decrease in the iteration of the training data, leading to achievement of stable convergence. To compare predictive accuracy of DLNs for sequence data processing, three deep neural models, namely RNN, LSTM, and GRU, were incorporated to examine the proper setting of the model parameters. First, the initial parameters for the three models from the transfer learning were regarded as the initial values and are listed in Table 8.  
In the experiment, the architecture parameters were determined based on the results of the Q-learning computation process. Tables 9 and 10 and Figures 9 and 10 show the corresponding convergence errors of the cost function for the RNN, LSTM, GRU and TCN prediction models for wind power forecasting 72 and 168 h ahead.