arXiv 2107.073422021-07cs.LGeess.IVphysics.optics

Probabilistic analysis of solar cell optical performance using Gaussian processes

Center for High Technology Materials（高技术材料中心）；University of New Mexico（新墨西哥大学）

Rahul Jaiswal, Manel Martínez-Ramón, Tito Busani

英文摘要：

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.

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