Jianfang Chen

Existing photovoltaic (PV) power forecasting methods face challenges in simultaneously capturing multi-scale temporal characteristics, spatial dependencies among PV plants, long-term temporal correlations, and output uncertainty. To address these issues, this paper proposes a spatiotemporal probabilistic forecasting framework for PV plant clusters that integrates Variational Mode Decomposition (VMD), Graph Convolutional Networks (GCN), Informer, and Quantile Regression (QR). VMD decomposes non-stationary PV power series into components with different frequency characteristics, while GCN captures spatial dependencies among PV plants. Informer efficiently models long-term temporal dependencies, and QR generates probabilistic forecasts to quantify output uncertainty. The proposed VMD-GCN-Informer-QR model is evaluated using data from the Australian DKASC PV system. At the 5-min forecasting horizon, the proposed model obtains an MAE of 42.834 kW and the lowest RMSE of 72.182 kW. For probabilistic forecasting, the proposed model achieves a PICP of 89.682%, with an MPIW of 201.818 kW. Multi-step forecasting further shows that the proposed model outperforms Persistence from 15 to 60 min, with its relative advantage increasing as the forecasting horizon extends. Seasonal analysis also confirms the adaptability of the proposed model under different seasonal conditions. These results demonstrate the effectiveness of the proposed framework in limiting large forecasting errors, quantifying forecasting uncertainty, and maintaining robust performance over extended forecasting horizons.