## 4 Experiment Design and Comparative Analysis
### Results and Discussion
The RMSE of the VMD-SSA-TCN-BiGRU model is 55.107, while the \(E_{RMSE}\) of the remaining models ELM, BiLSTM, BiGRU, Transformer, TCN, N-BEATSX, TCN-BiGRU, and SSA-TCN-BiGRU are 162.763, 156.92, 152.863, 190.164, 148.232, 127.764, 125.071, and 85.972, respectively. The VMD-SSA-TCN-BiGRU model's \(R^{2}\) reached 0.997. Based on the evaluation metrics in Table 2, it can be found that the proposed model in this paper outperforms the rest of the models in wind power prediction and can predict wind power more accurately. Figure 10 shows the scatter plots of the actual and predicted power of the nine models. The horizontal axis of each subgraph represents the actual power corresponding to the model, while the vertical axis represents the predicted power corresponding to the model, including ELM, BiLSTM, BiGRU, Transformer, TCN, N-BEATSX, TCN-BiGRU, SSA-TCN-BiGRU, and VMD-SSA-TCN-BiGRU, which visually shows that the deviation between the true and predicted values of the single model is larger. The deviation of the combined model between the actual and predicted values of VMD-SSA-TCN-BiGRU is significantly smaller and more concentrated than that of TCN-BiGRU and SSA-TCN-BiGRU, which can indicate that the proposed model in this paper has significant advantages in wind power prediction. To illustrate the effectiveness of the proposed model more intuitively, Figure 11 compares the eight models in terms of \(E_{SMAPE}\), \(E_{MAE}\), and \(R^{2}\). When comparing the combined TCN-BiGRU model to the best single model N-BEATSX described above, \(E_{SMAPE}\) decreases by 4.23%, \(E_{MAE}\) decreases by 4.91%, \(E_{RMSE}\) decreases by 2.11%, and \(R^{2}\) improves by 1.76%, which shows that TCN can effectively extract complex wind power timing features, and  
Figure 9: Forecasting curves of different models. BiGRU can learn dynamically changing wind power. After adding the SSA algorithm, \(E_{SMAPE}\) decreases by 24.35%, \(E_{MAE}\) decreases by 33.04%, \(E_{RMSE}\) decreases by 31.26%, and \(R^{2}\) improves by 1.22%, indicating that SSA can find the optimal model parameters and improve the model's performance. After adding VMD, \(E_{SMAPE}\) decreases by 13.24%, \(E_{MAE}\) decreases by 24.03%, \(E_{RMSE}\) decreases by 35.90%, and \(R^{2}\) improves by 0.60%, which shows that the VMD decomposition can effectively reduce the adverse effects of nonlinear and non-stationary characteristics of wind power on prediction. From the above chart, it can be observed that the hybrid VMD-SSA-TCN-BiGRU model proposed in this article has a significant decrease in the SMAPE, MAE, and RMSE, while the R2 improves. Compared to the current time series' new algorithm N-BEATSX [44], the TCN can extract deep-level features from the time series, while BiGRU can effectively predict wind power. Although TCN-BiGRU [31] can improve the accuracy of wind power prediction, due to the numerous hyperparameters in the model, it is difficult to find suitable parameters manually. Therefore, hyperparameter optimization algorithms are needed. This paper uses SSA to optimize the hyperparameters of the TCN-BiGRU model, which has a good global search ability and convergence. There are many factors that affect wind power, and the original wind power series fluctuates greatly. Therefore, it is necessary to decompose the original wind power data. This article uses VMD [45; 46] to decompose the  
Figure 10: Scatter plot of actual and predicted values. original wind power data, which can effectively reduce the interference of the wind power data and reduce the overall lag of the model prediction.