本文提出了一种基于空间编码图生成的分布式光伏集群短期概率预测方法。首先将分布式光伏集群按照其空间布局编码为二维矩阵，并在此基础上构建光伏出力特征图与天气特征图；随后，采用条件生成对抗网络作为预测模型，将集群短期功率预测问题转化为给定条件下未来多时段光伏出力特征图的生成问题，并可通过改变输入随机向量生成多种预测场景，进而构建概率预测结果；最后，从点对点误差、区间覆盖率、数据分布相似度三个方面定义指标，全面衡量预测结果的有效性。基于澳大利亚太阳能中心Alice Spring分布式光伏项目数据的算例结果表明，本文方法可充分挖掘集群内部的时空相关性，并一次性获得集群所有光伏单元的预测结果，具有更好的预测精度与计算效率。

A short-term probabilistic forecasting method for distributed photovoltaic (PV) clusters based on spatial encoding maps is proposed in this study. First, the spatial layout of the distributed PV clusters is encoded into a two-dimensional matrix, forming PV output feature maps and weather feature maps. Then, a conditional generative adversarial network is used as the forecasting model, transforming the short-term power forecasting problem of the cluster into a problem of generating future multi-period PV output feature maps under given conditions. By changing the input random vectors, multiple forecast scenarios can be generated, thus constructing probabilistic forecasting results. Finally, the effectiveness of the forecast results is comprehensively evaluated using three metrics: point-to-point error, interval coverage rate, and data distribution similarity. Case study results based on the Alice Spring distributed PV project at the Australian Solar Energy Centre indicate that the proposed method can fully exploit the spatiotemporal correlations within the cluster, simultaneously obtaining forecast results for all PV units in the cluster with good prediction accuracy and computational efficiency.