The Best of Two Worlds: Physics-Informed Neural Networks for Solar Energy Modeling

The growing worldwide penetration of solar energy into power grids has increased the need for accurate forecasting to reduce supply–demand imbalances and grid congestion. Physics-based models perform well for average conditions. However, edge cases such as very low or high irradiance are challenging for traditional physics-based models, which often underperform under these conditions. Data-driven models offer improved performance in these conditions but typically lack interpretability and insight into the underlying physical mechanisms. To address these limitations, this article presents a combined modeling framework that integrates physics-based principles with machine learning inspired by Physics-Informed Neural Networks (PINNs). The main novelty of this work is a machine learning architecture for solar energy power prediction that explicitly isolates angle of incidence losses and temperature losses in the form of the respective parameters entering a PV power equation as a strict output constraint, which improves interpretability and generalizability, while maintaining high forecasting precision. The methodology is demonstrated using only commonly available commercial system data and validated on both a small-scale research system and a commercial floating solar installation. The results demonstrate significantly improved accuracy over basic physics-based models (0.018 and 0.018 versus 0.023 and 0.029 normalized mean absolute error), particularly for non-standard conditions, and isolate key loss mechanisms.