| Title |
Prediction of I-V Characteristics in Si Solar Cells using Artificial Neural Network |
| Authors |
김경민(Kyeongmin Kim) ; 김성겸(Seonggyeom Kim) ; 이정은(Jungeun Lee) ; 이종환(Jonghwan Lee) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.2.27 |
| Keywords |
Solar cell; Parameter extraction; Translation equation; Artificial neural network |
| Abstract |
It is difficult to accurately predict the I-V characteristics in solar cells, because the solar cells models have nonlinear relationship for temperature and irradiation. For translation equations as a function of temperature and irradiation obtained through parameter extraction, it has been shown that there are differences between the model and experimental data. This paper presents a method for predicting I-V characteristics for temperature and irradiation using artificial neural networks, leading to accurate prediction of nonlinear characteristics. As a result, the error data are checked by comparing the translation equation and the artificial neural network with the experimental data. It is observed that artificial neural networks outperform the translation equations in predicting I-V characteristics. |