# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Ablation Experimental Results  
To validate the effectiveness of each component within the proposed model, an ablation study was conducted after optimizing the parameters. This study involved removing specific network components and assessing the impact on model performance under the same parameter settings used in the comparative experiments, effectively simulating the model's performance without these optimizations. The ablation study comprised five experiments, each removing one of the following components: TCN, REST-LSTM, GRU, an adaptive weighting network, and TSO. The performance impacts of these removals were quantitatively assessed using MSE, MAE, and \(R^{2}\) as the evaluation metrics. Changes in these metrics provided insights into the specific contributions of each network component to the model's accuracy. The results of these experiments are presented in Tables 6-8. Figures 12-14 illustrate the effects of removing each model component on the MSE, MAE, and \(R^{2}\) values across the three operational parameters: unit flow rate, guide vane opening, and the maximum water guide tile temperature. These visual representations help elucidate which components are critical for maintaining high accuracy and which may be less significant. This analysis not only underscores the importance of each network component but also aids in understanding how the synergistic integration of these components enhances the overall predictive capability of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Status** & **GRU** & **MSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline Unit Flow Rate & excluded & 536.378 & 14.428 & 0.801 \\ (m\({}^{3}\)/s) & included & 294.481 & 3.508 & 0.891 \\ \hline Unit Flow Rate & excluded & 536.378 & 14.428 & 0.801 \\ (m\({}^{3}\)/s) & included & 294.481 & 3.508 & 0.891 \\ \hline Guide Vane Opening & excluded & 14.176 & 2.082 & 0.862 \\ (degrees) & included & 8.943 & 0.553 & 0.913 \\ \hline Maximum Water Guide Tile Temperature & excluded & 0.257 & 0.410 & 0.974 \\ (\({}^{\circ}\)C) & included & 0.058 & 0.097 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The impact of the improved TSO network on model accuracy. Figure 14: The impact of each component of the model on the indicators MSE, MAE, and \(R^{2}\) in the maximum water guide tile temperature. Figure 13: The impact of each component of the model on the indicators MSE, MAE, and \(R^{2}\) in the guide vane opening. Table 6 verifies the impact of removing the TCN on the overall experimental accuracy. In the three prediction states, the experimental accuracy decreased to varying degrees after removing the TCN. In the unit flow rate, after removing the TCN, MSE and MAE increased from 294.481 and 3.508 to 523.024 and 14.300, respectively, indicating that both the maximum and the average prediction error increased. Additionally, the \(R^{2}\) decreased from 0.891 to 0.806. This shows that removing the TCN reduced the model's fitting effect. Similar changes occurred in the prediction of 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 11.933 and 1.515, respectively. For maximum water guide tile temperature, the MSE and MAE increased from 0.058 and 0.097 to 0.109 and 0.136, respectively. The \(R^{2}\) for guide vane opening decreased from 0.913 to 0.884, and for maximum water guide tile temperature, it decreased from 0.994 to 0.989. These changes indicate that the TCN effectively enhances the model's feature extraction capability by extracting features from the overall time perspective, thus improving the prediction accuracy. Table 7 demonstrates the effect of excluding the REST-LSTM network on the model's predictive performance. The hybrid deep learning model with the REST-LSTM network exhibited smaller maximum and average prediction errors and higher \(R^{2}\) when predicting unit flow rate, guide vane opening, and maximum water guide tile temperature.