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.
Skip Nav Destination
M. Euphrates Distribution,
Ministry of Electricity,
e-mail: ammar_awadh@yahoo.com
Article navigation
August 2015
Research-Article
Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network
Ammar Mohammed Ameen,
M. Euphrates Distribution,
Ministry of Electricity,
e-mail: ammar_awadh@yahoo.com
Ammar Mohammed Ameen
Department of Electrical Power Engineering,
Universiti Tenaga Nasional
,Kajang 43000
,Selangor
, Malaysia
Babylon Electrical Distribution Directorate
,M. Euphrates Distribution,
Ministry of Electricity,
Hillah 51001
,Babylon
, Iraq
e-mail: ammar_awadh@yahoo.com
Search for other works by this author on:
Jagadeesh Pasupuleti,
Jagadeesh Pasupuleti
Department of Electrical Power Engineering,
e-mail: jagadeesh@uniten.edu.my
Universiti Tenaga Nasional
,Kajang 43000
,Selangor
, Malaysia
e-mail: jagadeesh@uniten.edu.my
Search for other works by this author on:
Tamer Khatib,
Tamer Khatib
Department of Energy Engineering
and Environment,
e-mail: tamer_khat@hotmail.com
and Environment,
An-Najah National University
,Nablus 97300
, Palestine
e-mail: tamer_khat@hotmail.com
Search for other works by this author on:
Wilfried Elmenreich,
Wilfried Elmenreich
Institute of Networked and Embedded
Systems/Lakeside Labs,
e-mail: wilfried.elmenreich@aau.at
Systems/Lakeside Labs,
Alpen-Adria-Universität Klagenfurt
,Klagenfurt 9020
, Austria
e-mail: wilfried.elmenreich@aau.at
Search for other works by this author on:
Hussein A. Kazem
Hussein A. Kazem
Search for other works by this author on:
Ammar Mohammed Ameen
Department of Electrical Power Engineering,
Universiti Tenaga Nasional
,Kajang 43000
,Selangor
, Malaysia
Babylon Electrical Distribution Directorate
,M. Euphrates Distribution,
Ministry of Electricity,
Hillah 51001
,Babylon
, Iraq
e-mail: ammar_awadh@yahoo.com
Jagadeesh Pasupuleti
Department of Electrical Power Engineering,
e-mail: jagadeesh@uniten.edu.my
Universiti Tenaga Nasional
,Kajang 43000
,Selangor
, Malaysia
e-mail: jagadeesh@uniten.edu.my
Tamer Khatib
Department of Energy Engineering
and Environment,
e-mail: tamer_khat@hotmail.com
and Environment,
An-Najah National University
,Nablus 97300
, Palestine
e-mail: tamer_khat@hotmail.com
Wilfried Elmenreich
Institute of Networked and Embedded
Systems/Lakeside Labs,
e-mail: wilfried.elmenreich@aau.at
Systems/Lakeside Labs,
Alpen-Adria-Universität Klagenfurt
,Klagenfurt 9020
, Austria
e-mail: wilfried.elmenreich@aau.at
Hussein A. Kazem
Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received October 14, 2014; final manuscript received May 22, 2015; published online June 4, 2015. Editor: Robert F. Boehm.
J. Sol. Energy Eng. Aug 2015, 137(4): 041010 (5 pages)
Published Online: August 1, 2015
Article history
Received:
October 14, 2014
Revision Received:
May 22, 2015
Online:
June 4, 2015
Citation
Ameen, A. M., Pasupuleti, J., Khatib, T., Elmenreich, W., and Kazem, H. A. (August 1, 2015). "Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network." ASME. J. Sol. Energy Eng. August 2015; 137(4): 041010. https://doi.org/10.1115/1.4030693
Download citation file:
Get Email Alerts
Analysis of Erosion of Surfaces in Falling Particle Concentrating Solar Power
J. Sol. Energy Eng (April 2025)
Related Articles
View Factors Approach for Bifacial Photovoltaic Array Modeling: Bifacial Gain Sensitivity Analysis
J. Sol. Energy Eng (April,2025)
An Improved Model of Estimation Global Solar Irradiation From in Situ Data: Case of Algerian Oranie’s Region
J. Sol. Energy Eng (June,2020)
An Improved Method for Extracting Photovoltaic Module I – V Characteristic Curve Using Hybrid Learning Machine System
J. Sol. Energy Eng (October,2021)
Experiences With Using Solar Photovoltaics to Heat Domestic Water
J. Sol. Energy Eng (May,2003)
Related Proceedings Papers
Related Chapters
Neural Network Modeling in Nanotechnology: The Development of Nanostructures Produced by Molecular Beam Epitaxy for Infrared Applications
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Sandia Heat Flux Gauge Thermal Response and Uncertainty Models
Thermal Measurements: The Foundation of Fire Standards
Energy Balance for a Swimming Pool
Electromagnetic Waves and Heat Transfer: Sensitivites to Governing Variables in Everyday Life