Predicting Module Performance from Cell and Module Parameters Using Machine Learning

We use machine learning and device physics to analyze mass-produced solar modules, identifying factors that affect performance. Our approach is demonstrated by simulating 10,000 PERC solar cells and 2,000 half-cell modules using numerical device simulations. Our flexible approach can be applied to real data from production lines and scenarios.