# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Ablation Experimental Results
In the unit flow rate, the model without the REST-LSTM network had an MSE and MAE of 441.665 and 7.595, respectively, compared with 294.481 and 3.508 for the model with the REST-LSTM network. The \(R^{2}\) increased from 0.836 to 0.891. Similar changes occurred in the predictions for guide vane opening and maximum water guide tile temperature. For guide vane opening, the MSE and MAE increased from 8.943 and 0.553 to 12.096 and 1.554, respectively. For maximum water guide tile temperature, the MSE and MAE increased from 0.058 and 0.097 to 0.138 and 0.166, respectively. The \(R^{2}\) for guide vane opening decreased from 0.913 to 0.883, and for maximum water guide tile temperature, it decreased from 0.994 to 0.986. This indicates that the REST-LSTM network more effectively captures and maintains short-term dependencies by incorporating residual connections within each time step, thereby enhancing the accuracy of hydropower unit operation status prediction. From Table 8, it is evident that utilizing the GRU network significantly enhances prediction accuracy across all the prediction states. For instance, in predicting unit flow rate, the MSE, MAE, and R\({}^{2}\) values with the GRU network are 294.481, 3.508, and 0.891, respectively, whereas without the GRU network, these values are 433.367, 11.415, and 0.839. Similarly, for guide vane opening predictions, the GRU network yields the MSE, MAE, and R\({}^{2}\) values of 8.943, 0.553, and 0.913, compared with 16.485, 2.610, and 0.839 without it. For maximum water guide tile temperature predictions, the values with the GRU network are 0.058, 0.097, and 0.994, whereas without it, they are 0.127, 0.291, and 0.987. These results indicate that incorporating the GRU network increases the fitting coefficient R\({}^{2}\) and reduces MSE and MAE, thereby decreasing prediction errors and improving the fitting effect. This demonstrates that the GRU network enhances the model's perspective during prediction, improving its fusion ability. Table 9 examines the necessity of weighting the model's output. Without the adaptive weighting network, the parallel network's prediction results are directly summed for output. In the unit flow rate experiment, incorporating an adaptive weighting network reduced the model prediction's MSE and MAE from 347.023 and 7.864 to 294.481 and 3.508, respectively, indicating a decrease in prediction error. The R\({}^{2}\) increased from 0.871 to 0.891, showing an improved fitting effect. In the guide vane opening experiment, adding the adaptive weighting network decreased the MSE and MAE from 11.196 and 1.549 to 8.943 and 0.553, respectively, and increased the R\({}^{2}\) from 0.891 to 0.913. For the maximum water guide tile temperature experiment, adding the adaptive weighting network reduced the MSE and MAE from 0.180 and 0.334 to 0.058 and 0.097, respectively, and increased the R\({}^{2}\) from 0.982 to 0.994. These comparisons demonstrate that the adaptive weighting network reduces prediction errors and improves the fitting effect. Thus, it is necessary to adaptively  
weight the parallel network's output, as this allows the model to update the output weight during backpropagation for more accurate predictions. In addition, to verify the performance of the optimization network in the hybrid deep learning model, we conducted an ablation experiment by removing the optimization network. The results, presented in Table 10, indicate a reduction in the MSE for unit flow rate, guide vane opening, and maximum water guide tile temperature when the optimization network is included. Specifically, the MSE values decreased from 536.378, 14.176, and 0.257 to 294.481, 8.943, and 0.058, respectively. This demonstrates that the optimization network helps mitigate large prediction errors. Furthermore, the MAE values also showed a decrease across the three prediction states, from 14.428, 2.082, and 0.410 to 3.508, 0.553, and 0.097, indicating a reduction in average prediction errors.