## 1 Introduction  
With the increasing global energy demand and the depletion of fossil fuel reserves, the growing contradiction between the limited availability of fossil energy resources and the rising energy demand has become increasingly evident. Reducing reliance on non-renewable energy sources is essential for ensuring the sustainable development of society [1]. However, the generation of renewable energy, particularly photovoltaic (PV) power, is significantly influenced by climate and environmental factors, leading to inherent uncertainty and volatility in PV power output. This, in turn, presents challenges for the stable operation of power systems [2]. In response to these challenges, advancements in statistical methods and artificial intelligence (AI) technologies have facilitated the continuous development of various photovoltaic power forecasting approaches, which can be categorized into three timescales: medium- to long-term, short-term, and very short-term forecasts [3]. However, traditional statistical methods primarily rely on historical data and often fail to account for the impact of weather conditions. In contrast, AI-based techniques can directly predict PV power output by training on large datasets that incorporate both historical data and atmospheric variables, offering a more robust solution that does not require the extensive input data typical of statistical methods. In recent years, the rapid advancement of deep learning technologies has provided new opportunities for photovoltaic power forecasting. Common models include BackPropagation (BP) neural networks, support vector machines (SVMs), radial basis function (RBF) neural networks, and Long Short-Term Memory (LSTM) networks, all of which have been widely used in PV power forecasting and have yielded promising results [4]. For instance,the paper [5] proposed a short-term power forecasting method for new energy generation by selecting a wide range of meteorological features and optimizing the parameters of the SVM model. They employed Particle Swarm Optimization (PSO) to enhance the model's prediction accuracy for renewable energy generation. However, such methods often fail to adequately model the complex nonlinear temporal features and long-term dependencies inherent in wind and solar power generation. The authors of [6] conducted a short-term PV power forecasting study using a BP neural network optimized by Ant Colony Optimization (ACO). They incorporated gray relational analysis to identify key factors influencing PV generation, improving the model's prediction accuracy. Despite this, BP neural networks are prone to issues such as vanishing gradients and local optima, and they struggle to effectively model time-series data. These methods also do not fully exploit the potential of deep learning architectures, such as Recurrent Neural Networks (RNNs) or LSTMs, which are better suited for handling sequential data. The study [7] applied a bidirectional LSTM network to forecast PV power generation under various weather conditions, demonstrating that this approach outperformed traditional machine learning algorithms in terms of both accuracy and stability. However, their model did not fully address the generalization ability across different regions or for long-term forecasts, and it lacked an evaluation of robustness under extreme weather events or sudden changes. [PERSON] et al. used the Temporal Fusion Transformer (TFT) model for next-day PV power forecasting, combining attention mechanisms with multi-timescale temporal dynamics. Their results showed that the TFT model outperformed traditional algorithms in prediction accuracy across multiple facilities [8]. However, the model's high complexity and its substantial requirements for data, computation, and storage resources could present challenges in resource-constrained environments. Combining multiple network models to form a combinatorial neural network is gradually becoming a mainstream method to improve prediction accuracy.