With the transition to the new power system

renewable energy sources

particularly photovoltaic (PV) generation

have become the dominant power supplier. Accurate forecasting of PV output is critical for maintaining power system stability. However

most existing forecasting methods have mainly focused on temporal sequence modeling while overlooking the spatial feature information among PV clusters. To address this issue

this study proposes a distributed PV short-term forecasting model that integrates fuzzy C-means (FCM) clustering and spatiotemporal graph neural network. Firstly

distributed PV stations are clustered with the FCM algorithm

and then a graph structure is constructed. The graph attention network (GAT) is employed to capture spatial features among distributed PV stations

and the sequences are processed by the bidirectional gated recurrent unit (BiGRU) to extract temporal features. In addition

the grey wolf optimizer (GWO) is introduced to optimize the model hyperparameters. The experimental results based on real data from 10 distributed PV stations verify that the proposed forecasting method effectively integrates spatial and temporal features of distributed PV stations and improves the prediction accuracy by at least 6.7% compared with other models.