# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 6 Conclusions  
To accurately predict the operational status of hydropower units and ensure their stable operation and continuous power generation, this paper introduces a novel hybrid deep learning model. The model utilizes TCN for feature extraction from comprehensive time series data, effectively capturing temporal dynamics. These extracted features are then processed through the DPMI module that combines REST-LSTM and GRU. The enhanced REST-LSTM model incorporates residual connections that are specifically designed to improve the retention of short-term memory within sequence data. By integrating these connections, the model stabilizes the processing of immediate temporal information. Furthermore, the combination of REST-LSTM and GRU in the parallel network allows for a comprehensive capture and integration of temporal features from different aspects of the data. This integrated information is then further refined through an adaptive weighting network, which ensures that the model produces accurate and reliable outputs. The model also incorporates the tuna swarm optimization algorithm for parameter selection within the parallel network, ensuring optimal solutions are retained throughout the optimization process and thereby enhancing the final prediction accuracy. The experimental results from the data provided by the Huadian Power Science Research Institute demonstrate the potential and efficacy of the proposed hybrid deep learning model. It consistently outperformed three comparative models in terms of prediction accuracy for key metrics such as unit flow rate, guide vane opening, and maximum water guide tile temperature. Moreover, it showed superior consistency in prediction trends for unit flow rate and guide vane opening. The results also validate that a hybrid application of various deep learning methods tends to outperform individual prediction models. The present study has several limitations that should be acknowledged. First, the model's predictions are based solely on the temporal trends in individual variables without accounting for the potential influence of other related variables. This limitation may hinder the model's ability to capture complex interrelationships, potentially affecting state prediction in real-world applications. Additionally, while the TSO algorithm employed in this study has proven effective, it is computationally expensive. As a result, scaling to larger datasets or models with more hyperparameters would significantly increase the computational cost. Finally, although the proposed model has been successfully validatedon three different operational conditions within the same hydropower unit, its robustness across other operational environments remains to be explored and validated. In light of these limitations, several future research directions can be considered: First, future studies will focus on integrating additional features beyond the current state data, such as environmental conditions and external factors, to develop a more comprehensive model that captures the interactions between various operational parameters. Second, future work will explore more advanced optimization techniques to reduce computation time while maintaining or improving model accuracy. Technologies such as AutoML frameworks may offer promising alternatives. Finally, further efforts will be made to enhance the model's robustness by testing it in a broader range of operational environments and considering variability and noise in sensor data. This will ensure the adaptability and reliability of the model in real-world applications, particularly in other hydropower units. [PERSON] contributed to the conceptualization, methodology, and project administration. [PERSON] was responsible for software development, methodology, and drafting of the original manuscript. [PERSON] validated the model and contributed to data curation. [PERSON] and [PERSON] participated in the investigation. [PERSON] and [PERSON] handled the visualization, while [PERSON] led the funding acquisition and final manuscript review.