Modeling the Impact of Partial Shading on Photovoltaic Panels

Abstract

Partial shading is a critical challenge for PV systems, causing power loss and damaging hotspots; existing detection relies on expensive hardware or oversimplified analytical models that fail under dynamic conditions. This work presents an end-to-end machine learning approach for predicting shading percentage via regression. A custom MATLAB Simulink physics-based single-diode equivalent-circuit model generated >24 million data points, varying irradiance (200–1000 W/m²), temperature (10–50 °C), twelve series-parallel array configurations, and shading levels (0–100%). A context-aware feature-engineering step normalizes voltage, current, and power into scale-invariant ratios so one model generalizes across configurations. The model uses XGBoost regression to estimate PV output under partial shading, demonstrating that properly processed I–V curve data alone can quantify shading without costly specialized hardware — a practical tool for optimizing residential PV systems.