# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 1 Introduction
However, the RNNs suffered from gradient exploding and vanishing problems with long sequence inputs [8; 9]. The choice of an appropriate model for wind-power forecasting is discussed later. For the second question, the process of setting the hyperparameters requires expertise and extensive trial and error. Some drawbacks of the developments in wind power forecasting have been discussed, such as the use of manual trial and error for setting the initial parameters for the model in the training process. There are no simple and easy ways to set the hyperparameters. Recently, a reinforcement learning (RL)-based Q-learning algorithm [6; 15; 16] has shown great success in tuning hyperparameters in controlled environments, such as energy forecasting, Atari, robotic manipulation, and Go. The Q-learning algorithm uses a model predictive control process to optimise a reward by an agent interacting with the environment states and makes better decisions compared to the traditional trial and error approaches. To improve forecasting accuracy for wind turbines, this study proposes a DLN-based model [7; 8; 9; 10; 11; 12; 13] with a Q-learning algorithm for wind power forecasting to determine the hyperparameters for four competing models by minimising the prediction error. There are several options with regard to DLN model choice; here, four sequence-to-sequence models for deep learning were compared to examine the model performance and recommendationsfor wind power forecasting. In the experiment, four completing models were pre-trained using real historical climate data and power generation outputs of wind turbines from a wind power farm in the Chang-Bin industrial zone of Taiwan. The experimental results show that the TCN exhibited higher prediction accuracy compared to recognized recurrent networks, such as GRUs, LSTMs, and RNNs. In developing the proposed model, our work focused on three important aspects:  
* In this study, the proper setting of the hyperparameters for the developed model was investigated using the Q-learning algorithm by minimising the loss of the cost function in the learning process. * Five initial parameters for the developed model were analysed by incorporating the Q-learning algorithm in the learning process: (i) number of stacks, (ii) filter size, (iii) dilatation coefficient, (iv) activation function, and (v) optimiser to decide the proper setting of hyperparameters for model training by the input of 1-year dataset of wind farms from the Chang-Bin industrial zone (CBTP) for Tai-Power Corporation in Taiwan. * In our experiment, the TCN mode for 168 h (7 days) ahead, mean absolute percentage error (MAPE) = 1.41%, had the highest prediction accuracy, followed by GRU MAPE = 3.72%, LSTM mode MAPE = 6.99%, and RNN mode MAPE = 28.18%. The remainder of this paper is organised as follows. Section 2 reviews previous studies on the DLNs for sequence-to-sequence data and a reinforcement learning algorithm. Section 3 introduces the reinforcement learning framework and the proposed Q-learning approach for determining the proper hyperparameters for the wind-power forecast model. A performance analysis of the experimental results is presented in Section 4. Finally, Section 5 draws the conclusions.