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This research investigates how the number of data points and parameters create impact on predicting the power conversion efficiency (PCE) of perovskite solar cells (PSCs) using a random forest ML model. Three cases of a dataset, with 60, 50, and 40 data points, were analyzed, revealing that accuracy decreases with reduced data points. The model’s accuracy was found to be 95.73%, 92.88%, and 92.07% for the respective cases. Among all the parameters involved in a Perovskite Solar Cell study, the parameters ‘Voc’, ‘Isc’, ‘Jsc’, ‘Pmax’, ‘Vmax’, ‘Imax’, and ‘Aperture’ were identified as influential in predicting efficiency. This study aids in optimizing perovskite solar cells for enhanced efficiency and sustainable energy applications.

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A Study on Efficiency Prediction of Perovskite Solar Cell Using Random Forest

  • 会议论文

  • 发表日期：

    2026年

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  • Nita Samantaray,
  • Anu Tonk,
  • Arjun Singh

摘要

This research investigates how the number of data points and parameters create impact on predicting the power conversion efficiency (PCE) of perovskite solar cells (PSCs) using a random forest ML model. Three cases of a dataset, with 60, 50, and 40 data points, were analyzed, revealing that accuracy decreases with reduced data points. The model’s accuracy was found to be 95.73%, 92.88%, and 92.07% for the respective cases. Among all the parameters involved in a Perovskite Solar Cell study, the parameters ‘Voc’, ‘Isc’, ‘Jsc’, ‘Pmax’, ‘Vmax’, ‘Imax’, and ‘Aperture’ were identified as influential in predicting efficiency. This study aids in optimizing perovskite solar cells for enhanced efficiency and sustainable energy applications.