# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
###### Abstract
This model leveraged CNN's feature extraction and BiLSTM's temporal analysis to significantly enhance prediction accuracy. Similarly, [PERSON] et al. [26] combined CNN with GRU to predict wind patterns at canyon sites during hydropower construction, addressing the challenges of wind speed data analysis and ensuring operational safety. Moreover, [PERSON] et al. [27] developed a hybrid model using one-dimensional CNN and GRU to adaptively learn from operational and fault data, surpassing traditional machine learning models in diagnostic capabilities. Despite these advances, hybrid deep learning models integrating CNN and RNN variants have inherent limitations. For example, CNNs have a limited receptive field, restricting their ability to capture temporal dependencies and causal relationships in time series. Excessive nonlinear activation functions in LSTM can degrade the integrity of information over long sequences. The effectiveness of LSTM and GRU also heavily depends on selecting appropriate hyperparameters, which requires careful tuning. Lastly, the exploration of new deep learning architectures in hydropower prediction is still in its early stages, indicating ample room for innovation and improvement. To address these issues, this study introduced a hybrid deep learning model designed to enhance prediction accuracy and responsiveness in hydropower systems. The model integrates a Temporal Convolutional Network (TCN) to extract critical temporal features and a modified LSTM cell, termed Residual Short-Term LSTM (REST-LSTM), which incorporates a residual connection to improve the processing of short-term dependencies by directly adding the transformed input to the hidden state. Additionally, a GRU is employed in parallel with the REST-LSTM to capture short-term dependencies, offering an alternative processing path that may improve the model's flexibility in handling various temporal dynamics. The tuna swarm optimization (TSO) algorithm is then utilized to optimize the TCN, REST-LSTM, and GRU components. Comparative analysis demonstrates that this hybrid model outperforms traditional methods in predicting the key operational states of hydropower units, such as flow rate, guide vane opening, and maximum guide vane water temperature. The primary contributions of this research are as follows:  
1. Hybrid model architecture: The introduction of a hybrid deep learning model integrating TCN, REST-LSTM, and GRU, each tailored to capture distinct aspects of sequence data, enhancing prediction accuracy and precision for hydropower unit operational status. 2. Innovative LSTM modification: The development of REST-LSTM, which included a residual connection within the hidden state to enhance short-term memory processing, leading to more effective learning. 3. Empirical validation: Comprehensive evaluation of the model using data from Huadian Electric Power Research Institute, demonstrating its effectiveness and practical applicability. The remainder of this paper was organized as follows: Section 3 explores the proposed hybrid deep learning model, detailing the operational mechanics of each component. Section 4 describes the data sources and preprocessing techniques. Section 5 discusses the empirical test results, highlighting the model's performance across various scenarios. Section 6 concludes with a summary of the research findings and potential avenues for future investigation.