###### Abstract
Compared to single-site linear autoregressive models and variations of GBRT models, the multi-site model shows competitive results in terms of root mean squared error on all prediction ranges, while time series models and regression analysis methods have achieved certain success in prediction. However, in most cases, due to the instability of meteorological data and high sampling frequency, these methods are insufficient for accurate predictions. In recent years, artificial intelligence technology, as a new generation of technology that simulates the work of the human brain to solve complex problems, is increasingly being applied in production and daily life [24]. The bottleneck of photovoltaic power generation efficiency caused by the instability of meteorological factors is also expected to be solved through artificial intelligence algorithms. Due to its strong adaptability, self-learning ability, and ability to fit complex nonlinear relationships, artificial intelligence algorithms have begun to replace traditional time series models and regression analysis methods in complex photovoltaic power generation prediction. The artificial intelligence algorithm trains a model that can predict future power by taking as input historical data of collected power and meteorological factors that affect power without explicit expressions of mathematical relationships. [PERSON] et al. [25] proposed a support vector regression model to perform one-hour-ahead solar photovoltaic power prediction. Experiments proved that, compared to polynomial regression and Lasso, the SVR prediction model is superior in accuracy. However, the accuracy of SVR in complex nonlinear scenarios will encounter challenges and SVR can not dig deep long-term dependencies in photovoltaic time series data. Recently, a deep learning theory, proposed by [PERSON] et al. [26], has rapidly evolved and offered deeper and more powerful nonlinear network structures compared to traditional machine learning methods [27]. [PERSON] and [PERSON] [28] proposed using Long Short-Term Memory (LSTM) to predict photovoltaic power generation, which can effectively capture time continuity and period dependence. Experimental results show that LSTM has the highest prediction accuracy compared to recurrent neural networks (RNNs), generalized regression neural networks (GRNNs), and extreme learning machines (ELMs). However, LSTM, being a recursive model, requires long computation times and high computational performance, thus demanding high computer hardware requirements. In order to solve the computing time problem of LSTM, [PERSON] et al. [29] proposed a prediction framework based on an improved Temporal Convolutional Network (TCN) specifically for time sequence processing to predict wind power. This method, by expanding causal convolution and residual connections, solves the problems of long-term dependency and performance degradation of deep convolutional models in sequence prediction. Experimental results show that the TCN exhibits higher prediction accuracy than existing predictors (such as Support Vector Machines, Multilayer Perceptron, Long Short-Term Memory networks, and Gated Recurrent Unit networks). However, to capture long-term dependencies, TCNs usually require a larger receptive field, which may lead to increased computational complexity and decreased accuracy, especially when dealing with very long time series that are highly complex with dynamic, changing characteristics. Currently, in order to further improve the prediction accuracy and avoid the shortcomings of each single model, more and more researchers are using model combinations to predict photovoltaic power. [PERSON] et al. [30] proposed a prediction model based on CNN and LSTM, using an improved CNN layer to extract features, and the output results of the CNN were used to predict targets using a stacked LSTM network, achieving excellent prediction accuracy. [PERSON] et al. [31] proposed a new model using LSTM-TCN to predict photovoltaic power generation.