###### Abstract
This model combines Long Short-Term Memory and Temporal Convolutional Network models, utilizing LSTM to extract temporal features from input data and then integrating them with the TCN to establish a connection between features and output. Compared with LSTM and TCN, it reduced the Mean Absolute Error as follows: Autumn by 8.47%, 14.26%; Winter by 6.91%, 15.18%; Spring by 10.22%, 14.26%; and Summer by 14.26%, 14.23%. Although the combined model further improves the prediction accuracy, it also makes the model more complex and less interpretable, particularly as the data shape transformation into each model becomes more complicated. Recently, a new network of the RNN, the Simple Recurrent Unit (SRU), has been used for regression problems. The SRU network, by introducing parallel computing and GPU optimization, solves the problem of models with long training times and large occupation of computing resources, such as LSTM, TCN, and combined models, without losing accuracy. Additionally, it has better long-period acquisition and nonlinear processing capability than TCNs. The SRU network has recently been applied to complex nonlinear classification and regression problems, achieving commendable predictive results in water quality prediction [32], humidity in waterful breeding environments [33], remaining useful life prediction of bearings [34], and spatiotemporal traffic speed prediction in urban road networks [35]. Some scholars have also used outlier processing on datasets to reduce the complexity of the model and improve the prediction accuracy. In experiments conducted by [PERSON] et al. [36], assessing the performance of time series outlier detection techniques, the K-Nearest Neighbors (KNN) and the Fulford-Blasingame methods are better than other outlier detection methods among the 17 evaluated. Therefore, this study uses KNN for outlier processing of photovoltaic power data time series. Furthermore, some researchers have introduced intelligent optimization algorithms to enhance the performance of the proposed models, such as Particle Swarm Optimization (PSO) [37], the Grey Wolf Optimizer (GWO) [38], and the Cuckoo Search (CS) [39]. Experimental results indicate that the use of optimization algorithms can improve the predictive capability of the proposed models. At the same time, quite a few optimization algorithms have been applied to the photovoltaic field to solve corresponding problems, such as the parameter identification of solar cells using the improved Archimedes Optimization Algorithm [40], parameter extraction for photovoltaic models with tree seed algorithm [41], and parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution [42], these have all achieved certain success. Now, a new population-based optimization algorithm called the Hunter-Prey optimizer (HPO) was proposed by [PERSON] et al. [43] in 2022. It has the advantages of fast convergence and a strong optimization ability by simulating the animal hunting process. Many scholars have applied it to their research,and through the use of the HPO, they have avoided the uncertainty of manual and empirical adjustment of parameters and improved the accuracy and effectiveness of predictions such as the Short-Term Power-Load Forecasting Method Based on the HPO-LSTM Model [44]. Research on Early Warning of Coal and Gas Outburst Based on HPO-BiLSTM [45], and machine vision-based recognition of elastic abrasive tool wear and its influence on machining performance [46]. In view of the existing foundation of intelligent optimization algorithms in the photovoltaic field and the achievements of the HPO in other fields, this paper uses the HPO algorithm as the parameter of the intelligent optimization algorithm of the model to improve the accuracy and effectiveness of predictions.