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Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network

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dc.citedby20
dc.contributor.authorAmeen A.M.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorKhatib T.en_US
dc.contributor.authorElmenreich W.en_US
dc.contributor.authorKazem H.A.en_US
dc.contributor.authorid56602552200en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid31767521400en_US
dc.contributor.authorid6505948861en_US
dc.contributor.authorid24466476000en_US
dc.date.accessioned2023-05-29T06:00:06Z
dc.date.available2023-05-29T06:00:06Z
dc.date.issued2015
dc.descriptionErrors; Forecasting; Mean square error; Neural networks; Photovoltaic cells; Average deviation; Back propagation artificial neural network (BPANN); Mean absolute percentage error; Photovoltaic arrays; Photovoltaic systems; Prediction accuracy; Prediction model; Root mean square errors; Backpropagationen_US
dc.description.abstractThis paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system's output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model's accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, -0.21 A (-4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy. Copyright � 2015 by ASME.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo41010
dc.identifier.doi10.1115/1.4030693
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84930626324
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84930626324&doi=10.1115%2f1.4030693&partnerID=40&md5=9e111d2a8a8eab5ffbd01108f34674bc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22304
dc.identifier.volume137
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_US
dc.sourceScopus
dc.sourcetitleJournal of Solar Energy Engineering, Transactions of the ASME
dc.titleModeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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