太阳能具有清洁、安全、可再生的优点，光伏发电可减轻资源消耗，助力可持续发展，然而光伏功率易受天气影响，针对不同天气类型下光伏功率的预测也是一个研究难点。该研究着手于在不同天气类型下应用人工少数类过采样法(synthetic minority over-sampling technique，SMOTE)和机器学习进行光伏功率预测。首先，通过皮尔逊相关系数法选择出对光伏功率影响最大的气象因子；然后，根据重要程度较大的气象因子计算日照时数，通过给日照时数设定阈值进行划分，将天气分类为晴天、多云或阴天、覆雪，再通过SMOTE技术对各种天气类型下的样本进行扩充；最后，通过多种机器学习算法分别针对不同天气场景以及数据扩充前后构建光伏功率预测模型。通过案例分析可知，所提算法能对不同天气类型进行划分，并为不同天气类型下光伏功率预测存在的样本不平衡问题提供了一种解决方案，提升了不同天气场景下光伏功率的预测精度。

Solar energy has the advantages of being clean, safe, and renewable, and photovoltaic (PV) power generation can reduce resource consumption and contribute to sustainable development. However, PV power is easily affected by weather, and the prediction of PV power for different weather types is also a research difficulty. This study proceeds to apply synthetic minority over-sampling technique (SMOTE) and machine learning for PV power prediction under different weather types. Firstly, the meteorological factors that have the greatest impact on PV power are selected by the Pearson's correlation coefficient method. Then the sunshine duration is calculated based on the meteorological factors with a greater degree of importance, and the weather is classified as sunny, cloudy or cloudy, and snow-covered days by setting a threshold for the number of hours of sunshine, and then the samples under various weather types are expanded by the SMOTE technique. Finally, the PV power prediction model is constructed by various machine learning algorithms for different weather scenarios and before and after data expansion. Through case validation, it can be seen that the algorithm proposed in this paper is able to classify different weather types, and provides a solution to the sample imbalance problem of PV power prediction under different weather types, which improves the prediction accuracy of PV power under different weather scenarios.