# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 4 Experimental Results  
In this section, the performance of the proposed TCN-based model was demonstrated using an example of wind power prediction. The prediction system was executed in the Linux environment, and the software packages installed are listed in Table 4. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Item**} & \multicolumn{1}{c}{**Feature**} \\ \hline \# of Wind turbines & 31 units (2 MW) \\ \hline Wind turbine & Vestas V80/2000 wind turbine, Denmark \\ \hline Device capacity & 62 MW \\ \hline Rated power & 2000 kW \\ \hline Annual power generation & 135 million kWh \\ \hline \end{tabular}
\end{table} 
Table 3: The basic features of the wind turbines in the Chang-Bin industrial zone. Step 1. Data pre-processing phase  
In the experiment, model pre-training used a project dataset of wind farms from the Chang-Bin industrial zone (CBTP) for Tai-Power Corporation in Taiwan, including real weather observations and wind turbine power outputs to pre-train the model. Each record of the CBTP dataset contained five parameters sampled every 10 min with a total of 192,817 samples listed. The five parameters in the historical file are listed as follows:  
1. Date/time: 10 min intervals
2. Wind speed (m/s): The wind speed at the hub height of the turbine. 3. Wind direction (\({}^{\circ}\)): The wind direction at the hub height of the turbine
4. Real power output (kW): The power generated by the turbine for that moment. 5. Theoretical power curve (KWh): The theoretical power values that the turbine generates with that wind speed, which is generated by the turbine manufacturer. To increase the recognition accuracy, the correct value of null fields was mislabelled to build a clean dataset for the experiment. The data cleaning process in experimental data was manually pre-processed to remove null fields and were pre-filled in the linear proportions of the neighbouring observation data. The training dataset used samples collected every 10 min from 1 January 2018 to 31 December 2018. Further, the test dataset in the experiment was 7 days in total, from 1 January to 7 January 2019. Step 2. Model training phase  
To examine model efficiency, the experiment incorporated four deep neural models for series data processing, that is, GNN, LSTM, GRU, and TCN, to conduct wind power forecasting 24-168 h (1-7 days) ahead. First, a series of experiments were pre-trained to investigate the performance of the TCN-based classifier effectiveness using the training dataset, where the learning results were regarded as the basis for the proper selection of model parameters, including five important hyperparameters for structure design, i.e., the number of filters, dilatation coefficient, activation function, optimiser, and number of stacks. To achieve proper selection of hyperparameters in a TCN model, we used a reward function defined in Equation (10) for neural network generation and hyperparameter tuning based on Markov decision process. The decision process for generating a TCN architecture with the optimal parameters on neural network layers using Q-learning algorithm is shown in Figure 7[30]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Hardware/Software Installed** \\ \hline CPU & AMD Ryzen Threadripper, 1920X (3.4 G) \\ \hline OS & Ubuntu 14.04 LTS 64 \\ \hline GPU & Nvidia GTX-2080 TI x2 \\ \hline Language & Python 3.5 \\ \hline Library Package & TensorFlow-gpu 1.13, Pandas 0.23.4, Numpy 1.18, Matplotlib 3.3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Software packages installed in the wind power forecasting platform. Figure 7: Decision process for generating a TCN architecture. To improve input processing capability, the TCN model was developed to generate the receptive field to solve series data input problems. In particular, TCNs address the problems associated with dilated causal convolution, and residual connection.