为提高地表太阳辐射在复杂天气情况下的预报精确度并减小预报的时间成本，结合广州市白云区的历史气象数据，提出了一种以中国气象局的天气划分标准对历史天气进行分类的方法，并在各天气的子模型下使用支持向量回归(support vector regression, SVR)对地表辐照度进行预报。由于天气类型较多，因此对各子模型利用极限梯度提升(extreme gradient boosting, XGBoost)算法进行特征分析，并利用Mann-Whitney检验，合并了特征重要性类似的序列，实现了天气的二次分类，降低了模型的复杂度。结果显示，本文模型在连续12个月的预报中，相关系数、准确率和合格率均超过了评判指标的要求，具有较高的预测精度。且预报总计用时10.633 h，相比其余模型的13～34 h，预报速度更快，迎合了光伏电站中对太阳辐射预报的及时性的需求。

To improve the surface solar radiation forecasting accuracy in complicated weathers and to reduce the time-consuming cost, this paper proposes a method that using the China Meteorological Administration's weather classification criteria, to classify the historical meteorological data of Baiyun District, Guangzhou, into different weather types to forecast the surface solar radiation in sub models using support vector regression (SVR). Considering that the weather types are many, by analyzing the features of each sub model with extreme gradient boosting (XGBoost) algorithm and using Mann-Whitney test, series with similar feature importance are merged, thus achieving the secondary weather classification and reducing the complexity of forecasting model. Forecasting result by the model proposed shows that all the correlation coefficient, accuracy rate and qualification rate for the 12 successive months have exceeded the requirement of evaluation criteria, which guarantees a high accuracy. Besides, it totally consumes 10.633 hours to forecast, promising a faster forecasting method compared with the 13~34 hours of other models, which meets the demand for timely solar radiation forecasting in photovoltaic power plants.