Physics-Inspired Machine Learning Framework for Reliable Power Prediction in Photovoltaic Systems

Accurate forecasting of photovoltaic (PV) power is essential for the reliable operation of renewable energy systems. Conventional approaches fall into two extremes: physics-based models that provide theoretical accuracy but require extensive parameters, and data-driven machine learning models that can learn from historical trends but often behave as black boxes with limited interpretability. While machine learning has achieved strong short-term performance, its lack of physical grounding restricts generalization to unseen weather or operating conditions. This study introduces a physics-inspired machine learning framework for predicting PV power output under varying irradiance and temperature conditions. A dataset of PV system performance, meteorological variables, and solar irradiance is used to train a neural network. Unlike conventional models, the proposed framework integrates physics-informed regularization into the training process. Constraints derived from PV physics including the Shockley–Queisser efficiency limit, temperature dependence of bandgap energy, and diode-based current–voltage relations are embedded into the model’s loss function. These constraints ensure that predictions remain consistent with established photovoltaic principles while still adapting flexibly to real data. Preliminary evaluations show that the physics-regularized model reduces prediction error compared to standard neural networks and maintains robust performance under unseen conditions. More importantly, the approach offers interpretable and trustworthy forecasts, addressing a key gap in PV power prediction. By combining the adaptability of AI with the reliability of physical laws, this work contributes to the development of sustainable, data-driven tools for renewable energy system optimization.