## 1 Introduction  
Solar energy, as a renewable energy source that is inexhaustible and sustainable, holds a significant position in long-term energy strategies due to its ample cleanliness, relative abundance, low maintenance requirements, resource abundance, and potential economic benefits [1; 2]. Among various solar energy generation methods, photovoltaic power has garnered widespread attention in recent years [3]. With the increasing installed capacity and share, the stochastic and fluctuating characteristics of distributed photovoltaic systems have become impossible to overlook in terms of their impact on grid security dispatch and field operation management [4]. An accurate prediction of photovoltaic power generation can provide conventional power plants with sufficient time to start up and maintain proper reserves [5], thereby ensuring the safe and stable operation of the power grid and reducing operational costs. Related Work on PV Generation Forecasting  
Photovoltaic power prediction has been a hot topic in recent years. Many researchers have conducted extensive work in this area. Forecast periods in photovoltaic power prediction are categorized into four distinct time windows: ultra-short, short, medium, and long [6]. In these windows, ultra-short-term forecasts encompass predictions within an hour, crucial for immediate operational adjustments. Short-term forecasts, ranging from one hour to a full day, cater to daily management and responsiveness. The medium-term window, extending from a day up to several weeks or months, is vital for schedulingmaintenance and preparatory operations. Long-term forecasts, projecting several months to years ahead, are pivotal for strategic planning and participating in energy markets [7]. As to prediction techniques, the forecast methods can be broadly categorized into three types: physics-based methods, statistical methods, and hybrid methods. Physics-based methods [8] rely on meteorological data, geographical information, and detailed photovoltaic cell physical model information to simulate the power generation process. By studying the model's power generation process, they predict solar radiation intensity, and the power generation can be obtained from the predicted solar radiation intensity. This type of method typically does not require historical data. However, due to the challenge of obtaining accurate photovoltaic cell physical model data and the limited resolution of geographical information data, the accuracy of physics-based prediction methods may not be ideal. Statistical methods [9; 10; 11] work by analyzing a large amount of historical data and establishing inherent mapping relationships to directly predict photovoltaic power. However, due to the stochastic and fluctuating nature of photovoltaic power, the generalization ability of statistical methods may be reduced. Hybrid methods [12; 13] combine both physics-based and statistical approaches to leverage the strengths of each and address their weaknesses. These methods aim to improve prediction accuracy and robustness by integrating physical understanding with data-driven insights. Recently, deep learning [14; 15; 16] has attracted a great deal of attention. A study [17] proposed an RNN (recurrent neural network) model for solving complex nonlinear mapping problem. However, RNN often struggles with long-term data dependencies due to vanishing gradients. Another research study [18] employed a long short-term memory network (LSTM), successfully addressing these gradient issues inherent in traditional RNN. Despite its effectiveness, LSTM has its own set of limitations [9; 19]. A combination of a bi-directional long short-term memory (BiLSTM) network and a copula sampling method has been utilized to create representative scenarios for photovoltaic (PV) power production, as noted in references [20; 21]. Earlier, in [22], a generative adversarial network (GAN) was initially used for the generation of these PV power scenarios.