This paper presents a new I-V curve prediction method using artificial neural networks. The proposed method is based on two artificial neural networks namely generalized regression artificial neural network and cascaded forward neural network. An experiment is set up so as to extract a dataset that includes records of solar radiation, ambient temperature, current and voltage for different photovoltaic modules. The developed model is a general model for all photovoltaic modules whereas the inputs of the model are solar radiation, ambient temperature and datasheet specifications of photovoltaic module (open circuit voltage and short circuit current). Matlab is used to train, test and validate the proposed model. Moreover, the proposed model is validated experimentally. The results show that the proposed model has a high accuracy in predicting I-V curves with average mean absolute percentage error, mean bias error and root mean square error of 1.09%, 0.0229 A and 0.0336 A respectively. Such a model is very helpful in generating I-V curves for different photovoltaic modules. © 2018 Elsevier Ltd

Title

Solar Energy

Publisher

Elsevier Ltd

Publisher Country

Netherlands

Indexing

Thomson Reuters

Impact Factor

4.374

Publication Type

Prtinted only

Volume

173

Year

2018

Pages

462-469