Probabilistic Forecasting of Regional Photovoltaic Power Based on QR-STGAT

As the penetration rate of photovoltaic power generation continues to increase within new power systems, accurately forecasting regional PV power output has become critical to ensuring the safe and stable operation of power grids. Photovoltaic power generation exhibits significant spatio-temporal correlations, and traditional single-site forecasting methods struggle to fully capture the spatial dependencies among multiple PV plants within a region. To address this challenge, this study proposes a unified QR-STGAT probabilistic forecasting framework that jointly captures adaptive spatial dependencies via graph attention mechanisms and multi-scale temporal dynamics via a CNN-GRU architecture, while enabling end-to-end uncertainty quantification through integrated quantile regression. The framework is validated on 15 min resolution PV output data collected from five prefecture-level cities in Guangdong Province over a seven-month period from January to July 2025, and compared against baselines including BiLSTM and Transformer. Experimental results demonstrate that the proposed method reduces RMSE by up to 11.61% over baseline models and achieves a PICP of 93.05% at the 95% confidence level, providing a more reliable reference for power system dispatch decisions.