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

¹ Laboratory of Physical Chemistry of Materials LPCM, Ben M'Sick Faculty of Sciences, P. B. 7955.Bd. Driss El Harti. Hassan II University of Casablanca. Morocco
² Regional Center for Education and Training CRMEF-Beni-Mellal Khenifra. Mohamed V Street. BeniMellal, Morocco
³ School of sciences, Moulay Ismail University, Meknes, Morocco

^* Corresponding author: mohammedazza81@gmail.com

Abstract

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 I_ph