准确的分布式光伏超短期功率预测对于分布式光伏售电商参与电力现货市场起到重要的信息支撑作用。现有方法大多采用集群信息共享的建模方法以提升功率预测精度，然而这些方法或需依赖数据直接共享，导致严重的数据隐私问题，或仅依赖模型交互共享，虽保障数据隐私性但因其共享信息量薄弱导致预测效果不佳。为此，提出了一种基于隐私保护导向与深度联邦学习的分布式光伏超短期功率预测。首先构建了基于双层信息交互的联邦学习框架，以各站间全局模型与全局特征交互的形式实现更丰富的信息共享，避免了数据直接交互以保障数据隐私。其次以注意力自编码作为本地预测模型挖掘功率时序特征并将其与全局特征耦合为时空关联特征，通过有效挖掘利用时空关联信息从而提升功率预测精度。最后以河北某地区的实际分布式光伏数据进行仿真实验证明了所提方法的有效性。

Accurate ultra-short-term power prediction for distributed photovoltaic (PV) systems plays a crucial supporting role in providing essential information for distributed PV electricity sellers participating in the electricity spot market. Most existing methods employ cluster-based information-sharing modeling approaches to improve prediction accuracy. However, these methods either rely on direct data sharing, leading to serious data privacy issues, or depend solely on model interaction sharing.Although data privacy is ensured, the limited amount of shared information leads to poor prediction results. To address these challenges, a privacy-preserving, deep federated learning-based approach is proposed for ultra-short-term power prediction in distributed PV systems. Firstly, a federated learning framework based on dual-layer information interaction is constructed to enable richer information sharing through global model and global feature interactions among stations, avoiding direct data exchange to protect data privacy. Secondly, an attention-based autoencoder is adopted as the local prediction model to extract temporal features of power sequences, which are then coupled with global features to form spatiotemporal correlation features. By effectively mining and utilizing spatiotemporal correlation information, the power prediction accuracy is enhanced. Finally, simulation experiments using actual distributed PV data from a region in Hebei Province demonstrate the effectiveness of the proposed method.