为提高复杂天气条件下地表太阳辐射的预报能力，提出了一种基于分时预测与模型融合的预测方法。首先，通过补充天文辐射特征以反映太阳辐射强度的周期性，并结合天气分类方法引入辐照度衰减系数，对天文辐射进行修正，从而增强了相关性。其次，对数据进行了相关性分析，结果显示地表辐照度主要与天文辐射高度密切相关，而其他气象因素的相关性较低。因此，将数据集按时间节点划分为若干子集，以改变数据分布，从而提升其他关键特征之间的相关性。鉴于不同的数据集使用单一模型进行预测可能导致结果差异，为此采用Stacking算法来提高模型的泛化能力。同时，通过引入交叉验证和叠加高斯噪声的数据增广技术，实现对Stacking模型的优化。实验结果表明，优化后的Stacking模型能够有效提升泛化能力并降低过拟合风险；所提方法能有效识别复杂天气下地表辐照度的随机性，其准确率和合格率分别达到了95.8%和96.7%，相比传统预测方法提高了4%～19%。

To enhance the forecasting capability of surface solar radiation under complex weather conditions, a prediction method based on time-specific forecasting and model fusion is proposed. First, astronomical radiation features are supplemented to reflect the periodicity of solar radiation intensity, and a radiation attenuation coefficient is introduced through weather classification methods to correct astronomical radiation, thereby strengthening its correlation. Secondly, a correlation analysis of the data reveals that surface irradiance is primarily closely related to astronomical radiation height, while other meteorological factors exhibit lower correlations. Consequently, the dataset is divided into several subsets based on temporal nodes to alter data distribution and improve the correlation among other key features.Given that using a single model for predictions across different datasets may lead to discrepancies in results, we employ Stacking algorithms to enhance model generalization capabilities. Additionally, by incorporating cross-validation and augmenting data with added Gaussian noise techniques, we optimize the Stacking model.Experimental results indicate that the optimized Stacking model effectively improves generalization ability while reducing overfitting risks; this proposed method can accurately identify the randomness of surface irradiance under complex weather conditions with an accuracy rate of 95.8% and a qualification rate of 96.7%, representing an improvement of 4% to 19% compared to traditional forecasting methods.