# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
###### Abstract  
Hydropower units are integral to sustainable energy production, and their operational reliability hinges on accurate status prediction. This paper introduces an innovative hybrid deep learning model that synergistically integrates a Temporal Convolutional Network (TCN), a Residual Short-Term LSTM (REST-LSTM) network, a Gated Recurrent Unit (GRU) network, and the tuna swarm optimization (TSO) algorithm. The model was meticulously designed to capture and utilize temporal features inherent in time series data, thereby enhancing predictive performance. Specifically, the TCN effectively extracts critical temporal features, while the REST-LSTM, with its residual connections, improves the retention of short-term memory in sequence data. The parallel incorporation of GRU further refines temporal dynamics, ensuring comprehensive feature capture. The TSO algorithm was employed to optimize the model's parameters, leading to superior performance. The model's efficacy was empirically validated using three datasets--unit flow rate, guide vane opening, and maximum guide vane water temperature--sourced from the Huadian Electric Power Research Institute. The experimental results demonstrate that the proposed model significantly reduces both the maximum and average prediction errors, while also offering substantial improvements in forecasting accuracy compared with the existing methodologies. This research presents a robust framework for hydropower unit operation prediction, advancing the application of deep learning  
Traditional machine learning methods are extensively applied to evaluate and predict the operational status and power generation capabilities of hydropower stations. Prominent techniques include Support Vector Machine (SVM) [9], Artificial Neural Network (ANN) [10], and Gaussian Regression Process (GPR) [11]. Machine learning's predictive capabilities are often enhanced by signal processing methods like Ensemble Empirical Mode Decomposition (EEMD) [12], Variational Mode Decomposition (VMD) [13; 14], and Empirical Wavelet Transform (EWT) [15], which extract features, decompose signals, or reduce noise from time series data. Despite these advancements, traditional machine learning approaches require extensive feature engineering to handle complex and high-dimensional time series data, which can be cumbersome and error-prone. Understanding high-dimensional features, such as temporal correlations, is crucial for accurately predicting and evaluating changes in the operational status of hydropower stations over time. Deep learning, an advanced subset of machine learning, excels in extracting high-dimensional features from data, eliminating the need for laborious feature engineering. It surpasses the physical, statistical, and traditional machine learning methods, offering enhanced performance in various applications [16; 17]. Consequently, deep learning is increasingly adopted for forecasting power generation and operational status in hydropower stations. Models such as Convolutional Neural Networks (CNNs) [18; 19] and recurrent neural networks (RNNs) [20] are predominantly utilized due to their effectiveness in processing complex time series data. CNNs are renowned for their ability to extract pertinent information from time series, identifying the key patterns essential for predictive accuracy [21]. RNNs are adept at recognizing temporal dependencies [22], but often face challenges like gradient explosion or disappearance during training. This has led to the increased use of their evolved forms, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), which offer more robust frameworks for learning from time series data [23; 24]. Combining CNNs and RNNs has proven particularly effective. For instance, [PERSON] et al. [25] successfully applied a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model to the Runoff Small Hydropower (RSHP) database, demonstrating commendable performance even with limited data.