## 3 Proposed Model
### Model Structure and Algorithm Flow  
Numerous factors affect wind power, and it is difficult for the traditional single model to effectively extract the temporal features of wind power. To solve the above problems, this paper proposes a combined TCN-BiGRU model, which enables the model to effectively mine the features of time series data. As Figure 5 shows, the structure of the TCN-BiGRU model consists of two layers of TCN blocks, two layers of BiGRU, and fully connected layers. Initially, the TCN block extracts the time series feature information, and the feature matrix extracted by the TCN is input into the BiGRU. The bi-directional information flow of time series information is captured through the BiGRU to learn the dynamic change pattern of wind power features. Finally, the dimensional change is performed through the fully connected layer, so it is possible to effectively predict the wind power at the next moment.  
As the TCN-BiGRU model features numerous hyperparameters, and manually adjusting the hyperparameters of the model has a significant impact on the prediction results, it is proposed to use SSA [36; 37; 38] to find the number of iterations, the learning rate, and the number of neurons and find the convolution kernel size in TCN-BiGRU to obtain the optimal model and improve the prediction accuracy.  
The model optimization process can be divided into the following steps:  
1. Determine the TCN-BiGRU parameters to be optimized and initialize the population size of the sparrows, the maximum number of iterations, the percentage of discoverers, the percentage of followers, the warning value, the initial positions of the sparrows, and the optimization interval of the hyperparameters.
2. Calculate the initial fitness of the individual sparrows and save the optimal fitness and the corresponding sparrows' positions.
3. Update the positions of the discoverers, followers, and alerters.  
Figure 5: TCN-BiGRU model structure.  
4. Calculate the fitness of the sparrow population and update the optimal fitness and sparrow positions.
5. Judge whether the optimal fitness is satisfied or whether the maximum number of iterations is reached. If the condition is satisfied, the optimal parameters are obtained. Otherwise, proceed to step 3.  
Since wind power generation is nonlinear and non-stationary, the original wind power generation is first decomposed into multiple subsequences and residuals through signal decomposition. Then, these subsequences and residuals are used as the inputs of the model for wind power generation prediction to effectively reduce the adverse effects of the nonlinear and non-stationary characteristics of wind power generation on the prediction model and improve the prediction accuracy. To this end, the paper introduces VMD to the above model and constructs the VMD-SSA-TCN-BiGRU model, whose structure is shown in Figure 6 below, and its algorithmic flow is as follows:  
1. Data preprocessing. Since the data extracted from the SCADA system will have some missing values, the original missing values are filled to form the complete time series data.
2. The power in the original data is decomposed into several components and residuals via VMD, which was introduced in Section 2.1. Each component and residual are combined with the remaining features of the wind power data to form new data, and then the data are normalized to divide the training and testing sets.
3. The parameters of SSA and TCN-BiGRU are initialized, the composed new data are inputted into the TCN-BiGRU model as shown in Figure 5, and the number of iterations, learning rate, number of neurons, and a convolution kernel size of the TCN-BiGRU are optimized via SSA to obtain the optimal model parameters and improve the prediction accuracy.
4. The predicted values of all the subsequences are accumulated to output the final results of the model's one-step-ahead prediction, and the constructed model is evaluated.  
Figure 6: VMD-SSA-TCN-BiGRU model structure.