E3S Web of Conferences (Jan 2021)

Automatic solution for solar cell photo-current prediction using machine learning

  • Azza Mohammed,
  • Daaif Jabran,
  • Aouidate Adnane,
  • Chahid El Hadi,
  • Belaaouad Said

DOI
https://doi.org/10.1051/e3sconf/202129701029
Journal volume & issue
Vol. 297
p. 01029

Abstract

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In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

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