## 1 Introduction  
With the depletion of fossil fuels and the increasing severity of environmental pollution, the development of renewable energy has become an urgent task. Wind and solar energy, as green and pollution-free renewable energy sources, have seen a continuous rise in grid penetration [1, 2, 3]. To effectively combat the climate change and energy crisis, countries around the world are vigorously advancing the low-carbon, clean, and sustainable transformation of energy systems to address global climate change, environmental pollution, and energy strategies [4]. In this context, generating time-series power curves for wind and solar energy based on meteorological characteristics under natural conditions is essential for providing reliable data support for power grid planning [5]. In the current research on wind and solar power forecasting, effectively utilizing Numerical Weather Prediction (NWP) data is key to improving prediction accuracy [6; 7; 8]. NWP data provide various meteorological data including wind direction, wind speed, and so on, but not all the features are useful for forecasting. Therefore, it is necessary to extract, select, and optimize sensitive meteorological features from NWP data to improve prediction accuracy and reduce model complexity. Feature selection methods can be categorized as the following two types [9]: the first directly selects important features for model construction without altering the feature data, and the second maps features into a lower-dimensional space based on data relationships, which is more suited for large datasets with strong correlations. However, due to the significant variation in NWP data features, it can introduce noise, ultimately hindering prediction accuracy. Reference [10] presented a method for ultra-short-term wind speed correction based on NWP data. Reference [11] introduced a short-term wind power forecasting method that improved prediction accuracy by detecting and correcting errors within NWP data, thereby enhancing the reliability of short-term wind power forecasts. Reference [12] utilized the information entropy-based feature selection principle to effectively select meteorological features that significantly impact power output. Most current studies on feature selection rely on single correlation evaluation metrics, such as Pearson, Spearman, or information entropy. However, using a single metric may introduce biases, leading to errors in feature selection. Current research on modeling time series power curves for solar and wind energy is typically classified into two primary categories: mechanism-driven methods and data-driven methods [13]. Mechanism-driven methods are based on the fundamental principles of wind and solar power generation, calculating output power through physical formulas and key meteorological parameters such as wind speed and solar radiation [1]. However, the complexities of actual operations are always overlooked by these methods, resulting in smoother simulated curves that differ significantly from real conditions. On the other hand, data-driven methods utilize artificial intelligence techniques to uncover the complex nonlinear relationships between meteorological features and power output from measured data, generating power curves that are more reflective of reality [14; 15]. Nevertheless, data-driven methods require a large amount of historical data, and their performance is limited when data are scarce. To address this issue, transfer learning is introduced by migrating a pre-trained wind and solar power output model, developed using data-rich sites, to newly established sites with similar characteristics but limited data availability. This approach not only mitigates the problem of data scarcity and enhances prediction accuracy but also significantly accelerates model training [16].