Constraint-Guided Residual PatchTST With NWP Features for Multi-Horizon Probabilistic PV and Wind Power Forecasting
Accurate renewable power forecasting is essential for grid operation, reserve scheduling, and renewable integration. This paper proposes a constraint-guided residual forecasting framework that combines a domain-informed baseline with data-driven residual correction for multi-horizon probabilistic forecasting. A Gradient Boosting Regression Tree (GBRT) model is used as a classical residual benchmark, while a Residual PatchTST model captures temporal dependencies from numerical weather prediction features, engineered time variables, and site information. Final forecasts are reconstructed by adding the predicted residual to the baseline and enforcing nonnegative outputs within site-level capacity limits. Across PV sites, GBRT reduces mean RMSE from 0.0909 to 0.0798 and mean MAE from 0.0400 to 0.0358, while wind RMSE decreases from 0.2574 to 0.1479. The deep probabilistic model also delivers stable multi-horizon performance and useful uncertainty intervals. These results show that residual learning with constraint-guided reconstruction provides accurate and operationally meaningful renewable power forecasts.