Probabilistic analysis of solar cell optical performance using Gaussian processes
This work addresses performance prediction for silicon-based textured solar cells, offering incremental improvements through uncertainty quantification in machine learning applications.
The paper tackled the problem of predicting solar cell optical performance by applying Gaussian processes to estimate reflection profiles and optical generation profiles with quantified uncertainty, achieving accurate predictions and enabling cell design parameter estimation for desired performance metrics.
This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells. Concept of confidence bound regions is introduced and advantages of this concept are discussed in detail. Results show that reflection profiles and depth dependent optical generation profiles can be accurately estimated using Gaussian processes with exact knowledge of uncertainty in the prediction values.It is also shown that cell design parameters can be estimated for a desired performance metric.