Cross-Site Physics-Informed Transfer Learning for One-Hour-Ahead Photovoltaic Power Forecasting

Accurate short-term photovoltaic power forecasting is essential for grid scheduling, reserve allocation, and the reliable integration of renewable energy into power systems. However, many data-driven forecasting models are trained and evaluated at a single photovoltaic plant, which limits their transferability to plants operating under different climatic, geographic, and engineering conditions. This study evaluated a cross-site physics-informed transfer-learning framework for one-hour-ahead photovoltaic power forecasting across six heterogeneous solar plants. The framework integrated a hybrid temporal model with plant-level static descriptors and a physics-based power prior derived from irradiance, module temperature, installed capacity, and grid export limits. The primary experiment used six leave-one-site-out target folds: for each held-out target plant, the other five plants were pooled as the source domain for pretraining. A separate, ordered single-source-to-single-target analysis (30 routes) was used solely to examine pairwise source-target distances and route-dependent transfer behavior. Source-domain and target-domain validation data were kept separate during checkpoint selection. Compared with direct transfer and conventional fine-tuning in the primary held-out-target analysis, the full physics-informed transfer model reduced forecasting error across the evaluated target sites. The study provides a structured framework for cross-site photovoltaic forecasting under heterogeneous environmental and operational conditions. Because inferential comparisons were based on six held-out target sites, they were interpreted as site-level evidence rather than evidence of broad population-level statistical generalizability.