## 4 Training Process of the Model
### Training Process  
The structure of the deep TPA-BiGRU network model proposed in this paper is mainly divided into the input, hidden, TPA, and output layers. The data input size of the input layer is \(151\times 4\), and the hidden layer has four layers, each containing a BiGRU layer, a dropout layer, and an activation function. For the classification network, its output layer outputs a sequence of length \(3\), and the activation function is Softmax. For the regression network, its output is the energy storage action value, and the length is \(1\). So its output layer can be a fully connected layer with the number of neurons \(1\). The specific hyperparameter settings are shown in Table 2.  
The hyperparameters listed in Table 2 are selected through empirical validation and domain-specific considerations. For instance, the dropout rate is tuned based on task complexity: a higher rate (\(0.5\)) applies to classification to counteract overfitting in multi-class scenarios, while a lower rate (\(0.3\)) is used for regression to preserve network capacity. Neurons in BiGRU layers are sized to balance computational efficiency and feature representation needs (\(256\) for classification vs. \(128\) for regression). The choice of sliding window size balances computational efficiency and feature richness, and the performance metrics of different window sizes are evaluated using sensitivity analysis, which shows that 151 \(\times\) 4 has the lowest RMSE. The number of BiGRU layers is investigated using an ablation study to quantify the depth-influence relationship, and the 4-layer optimization strikes a balance between accuracy and GPU memory utilization. All the choices are validated via cross-validation on our dataset, with ablation studies confirming their necessity. The data processing and training processes for the three TPA-BiGRU network models are as follows:  
1. Data preprocessing: Standardize and encode the input and output of the three networks, respectively. 2. Data sampling: The time series data after preprocessing are sampled by sliding sampling with a size of 151 \(\times\) 4 window and stored in the form of \(n\times\) 151 \(\times\) 4. 3. Division of training and test sets: The sampled dataset is divided into training and test sets in the ratio of 7:3. The data from the training set is fed into the TPA-BiGRU model, and the predicted values are obtained after neural network black-box computation. 4. Parameter update: The training set loss is calculated according to the predicted value and the training set label, and the parameters in the recurrent neural network are updated after a single back-propagation computation. 5. Performance evaluation: The test set loss is obtained by substituting the test set data into the untrained TPA-BiGRU model and comparing it with the training set loss. If overfitting or underfitting occurs, the network structure or hyperparameters need to be adjusted. After iterative training, three TPA-BiGRU models are constructed, laying the foundation for the simulation of the wind storage joint control strategy based on TPA-BiGRU. The training results are shown in Table 3.  
During the simulation process, the data are read in 151 \(\times\) 4 at the ordered moments and the raw data are normalized using Z-score and one-hot encoding codes. Then, they are inputted into the BiGRU network to extract the bidirectional timing features to memorize the timing relationship between the input variables, and the feature matrix in the last layer of the BiGRU network is inputted into the TPA network to streng