Physics-guided temporal fusion transformer for probabilistic photovoltaic power forecasting under weather regime shifts

Abstract

Accurate short-term photovoltaic (PV) power forecasting is critical for secure grid operation and economic dispatch, yet its performance is often degraded by non-stationary irradiance fluctuations induced by cloud transients and weather regime shifts. To address this challenge, this paper proposes a physics-guided temporal fusion architecture built on the Temporal Fusion Transformer (TFT) for multi-horizon probabilistic PV forecasting. The model fuses historical plant measurements, Numerical Weather Prediction (NWP) variables, and solar-geometry features, and introduces a cross-attention fusion module to align future meteorological drivers with the most relevant historical context for improved ramp responsiveness. Physical plausibility is promoted by soft physics-consistency regularization, including non-negativity, rated-capacity bounds, clear-sky envelope constraints, and ramp-rate penalties, while uncertainty is quantified via multi-quantile regression to produce calibrated prediction intervals. Experiments on real-world PV datasets across different seasons and weather conditions show that the proposed approach consistently improves deterministic accuracy (MAE and RMSE) and probabilistic performance (PICP and CRPS) over representative baselines, with particularly robust behavior under regime-shift and ramp events. Overall, combining attention-based temporal fusion with physics-guided learning provides a practical and scalable solution for reliable probabilistic PV power forecasting in highly variable atmospheric conditions.