Caracterización de celdas solares de perovskita mediante un análisis exploratorio de datos de variables eléctricas, band gap y área
DOI:
https://doi.org/10.15665/rp.v22i2.3485Keywords:
Exploratory data analysis, machine learning, perovskite solar cell, energy conversion factor.Abstract
Understanding the behavior of electrical variables in a solar cell contributes to determining its performance, thus patterns are sought to analyze intrinsic material variables and the influence of other variables such as area. Exploratory data analysis is used in information processing to detect patterns and relationships that can be exploited in the development of artificial intelligence. This article aims to characterize single junction perovskite solar cells through an exploratory analysis of the dataset containing electrical variables, band gap, and area from The Perovskita Database. This is achieved using univariate and multivariate visualizations such as violin plots and correlation plots respectively, which relate the mean, median, maximum, and minimum to detect patterns in perovskite solar cell technology and the relationships between the selected variables. The results show practical values of 0.9 V for the open-circuit voltage (Voc), 17.9 for the current density (Jsc), and 1.6 eV for the band gap for perovskite. It is concluded, given the contrast of practical values with the Shockley Queisser theory, that the Voc, Jsc, and band gap variables are intrinsic to single junction perovskite solar cells.
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