## 4 Experiment Design and Comparative Analysis
### Results and Discussion  
In this study, the dataset is split into a 70% training set and a 30% testing set. The step size is set to 20. This means that the historical data from \(t\)-20 to \(t\) moments are used to predict the wind power at \(t\)+1 moments. The proposed VMD-SSA-TCN-BiGRU model is initialized with the following parameters: The number of decompositions of VMD is 8. The population size of the SSA algorithm is 30. The producers account for 20%, the number of iterations is 20, and the search dimension is 4, which are the number of iterations, the number of neurons, the learning rate, and the convolutional kernel size. The search range of the number of iterations is [50; 300], the search range of the neurons is [50; 150], the search range of the learning rate is [0.001, 0.01], and the search range of the convolutional kernel size is [2; 30]. The expansion factor in TCN is [1; 2; 4; 8; 16], the size of the convolutional filter is 64, and the parameter optimization algorithm obtains the convolutional kernel size. The BiGRU has two layers of the network. The number of neurons is obtained using the optimization algorithm, Adam is used as the optimizer, the batch_size is 512, and mean square error is used as the loss function. To verify the validity of the model proposed in this paper, comparison experiments are performed with the ELM [39; 40], BiLSTM, BiGRU, Transformer [41; 42], TCN, N-BEATSX [43], TCN-BiGRU, and SSA-TCN-BiGRU models. The evaluation metrics \(E_{SAME}\), \(E_{MAE}\), \(E_{RMSE}\), and \(R^{2}\) are used in Table 2 to evaluate the advantages and disadvantages of each model. Figure 9 shows the fitted curves of the actual and predicted values as well as the local enlargement. Figure 9 shows the fitting curves of the different models. The wind power prediction curves for the last 5 days and different time periods are drawn. It can be found that the overall trend of the predicted and actual values of the nine models is roughly the same. However, it can be seen that the fitting performance of ELM, BiLSTM, BiGRU, TCN, N-BEATSX, and Transformer for a single model is not as good as that of the combined TCN-BiGRU model, and the combined SSA-TCN-BiGRU model is closer to the actual value than the TCN-BiGRU model. After adding VMD, it is evident that it will reduce the overall lag of the prediction results and improve the prediction curve fitting results. It can be found that the overall prediction results of the VMD-SSA-TCN-BiGRU model proposed in this paper are better than those of the rest of the models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(E_{\text{SMAPE}}\) & \(E_{\text{MAE}}\) & \(E_{\text{RMSE}}\) & \(R^{2}\) \\ \hline ELM & 6.765 & 116.351 & 162.763 & 0.938 \\ BiLSTM & 6.312 & 109.581 & 156.924 & 0.957 \\ BiGRU & 5.942 & 108.621 & 152.863 & 0.958 \\ Transformer & 7.403 & 136.527 & 190.164 & 0.930 \\ TCN & 5.805 & 103.863 & 148.232 & 0.962 \\ N-BEATSX & 4.138 & 80.315 & 127.764 & 0.975 \\ TCN-BiGRU & 3.963 & 76.375 & 125.071 & 0.979 \\ SSA-TCN-BiGRU & 2.998 & 51.137 & 85.972 & 0.991 \\ VMD-SSA-TCN-BiGRU & 2.601 & 38.845 & 55.107 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance index prediction results. This paper performs several experiments using identical model parameters and calculates the average value based on 10 trials. Table 2 shows the comparison of the evaluation metrics of different models. The \(E_{SMAPE}\) of the VMD-SSA-TCN-BiGRU model is 2.601, while the \(E_{SMAPE}\) of the remaining models ELM, BiLSTM, BiGRU, Transformer, TCN, N-BEATSX, TCN-BiGRU, and SSA-TCN-BiGRU are 6.765, 6.312, 5.942, 7.403, 5.805, 4.138, 3.963, and 2.998. The \(E_{MAE}\) of the VMD-SSA-TCN-BiGRU model is 38.845, while the remaining models ELM, BiLSTM, BiGRU, Transformer, TCN, N-BEATSX, TCN-BiGRU, and SSA-TCN-BiGRU have \(E_{MAE}\) values of 116.351, 109.581, 108.621, 136.527, 103.863, 80.315, 76.375, and 51.137. The proposed model has the most petite \(E_{MAE}\). The \(E_{RMSE}\) is highly variable.