Open AccessSilicon PV module fitting equations based on experimental measurements
Author(s) -
Sabry Ahmad H.,
Hasan Wan Z. W.,
Sabri Yasameen H.,
AbKadir Mohd Zainal Abidin
Publication year - 2019
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.264
Subject(s) - mean squared error , nonlinear system , artificial neural network , exponential function , photovoltaic system , polynomial , gaussian , computer science , algorithm , mathematics , function (biology) , gaussian function , curve fitting , mathematical optimization , artificial intelligence , statistics , mathematical analysis , machine learning , engineering , physics , quantum mechanics , evolutionary biology , electrical engineering , biology
Solar photovoltaic ( PV ) characteristic curves (P‐V and I‐V) offer the information required to configure the PV system to operate as near to its optimal performance as possible. Measurement‐based modeling can provide an accurate description for this purpose. This work analyzes the PV module performance and develops a mathematical formula under particular weather conditions to accurately express these curves based on a custom neural network ( CNN ). The study initially presents several standard mathematical model equations, such as polynomial, exponential, and Gaussian models to fit the PV module measurements. The model selection is subjected to the minimum value of an evaluation parameter. To simplify the solution of the symbolic equations for the CNN network, two neurons in the hidden layer with nonlinear activation function and linear for the output layer were selected. The results show the effectiveness of the proposed CNN model equations over other standard fitting models according to the root mean squared error ( RMSE ) evaluation. This method promises further improved results with multi‐input parameter modeling.