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Silicon PV module fitting equations based on experimental measurements

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dc.citedby5
dc.contributor.authorSabry A.H.en_US
dc.contributor.authorHasan W.Z.W.en_US
dc.contributor.authorSabri Y.H.en_US
dc.contributor.authorAb-Kadir M.Z.A.en_US
dc.contributor.authorid56602511900en_US
dc.contributor.authorid57219410727en_US
dc.contributor.authorid57202008988en_US
dc.contributor.authorid25947297000en_US
dc.date.accessioned2023-05-29T07:27:15Z
dc.date.available2023-05-29T07:27:15Z
dc.date.issued2019
dc.descriptionMathematical models; Mean square error; Nonlinear equations; Photovoltaic cells; Silicon; Solar power generation; Characteristic curve; Evaluation parameters; I - V curve; Mathematical formulas; Measurement based model; Nonlinear activation functions; Root mean squared errors; Solar photovoltaics; Curve fittingen_US
dc.description.abstractSolar 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. � 2018 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1002/ese3.264
dc.identifier.epage145
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85062024055
dc.identifier.spage132
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062024055&doi=10.1002%2fese3.264&partnerID=40&md5=f3f10bdc02b751b8ddbc86a398478589
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24799
dc.identifier.volume7
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergy Science and Engineering
dc.titleSilicon PV module fitting equations based on experimental measurementsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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