Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network
Errors; 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; Backpropagati...
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American Society of Mechanical Engineers (ASME)
2023
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my.uniten.dspace-223042023-05-29T14:00:06Z Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network Ameen A.M. Pasupuleti J. Khatib T. Elmenreich W. Kazem H.A. 56602552200 11340187300 31767521400 6505948861 24466476000 Errors; 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; Backpropagation This 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. Final 2023-05-29T06:00:06Z 2023-05-29T06:00:06Z 2015 Article 10.1115/1.4030693 2-s2.0-84930626324 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930626324&doi=10.1115%2f1.4030693&partnerID=40&md5=9e111d2a8a8eab5ffbd01108f34674bc https://irepository.uniten.edu.my/handle/123456789/22304 137 4 41010 American Society of Mechanical Engineers (ASME) Scopus |
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Errors; 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; Backpropagation |
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56602552200 Ameen A.M. Pasupuleti J. Khatib T. Elmenreich W. Kazem H.A. |
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Ameen A.M. Pasupuleti J. Khatib T. Elmenreich W. Kazem H.A. |
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Ameen A.M. Pasupuleti J. Khatib T. Elmenreich W. Kazem H.A. Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| author_sort |
Ameen A.M. |
| title |
Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| title_short |
Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| title_full |
Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| title_fullStr |
Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| title_full_unstemmed |
Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| title_sort |
modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network |
| publisher |
American Society of Mechanical Engineers (ASME) |
| publishDate |
2023 |
| _version_ |
1806428427512709120 |
| score |
13.222552 |