Research Papers

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

[+] Author and Article Information
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,
Universiti Tenaga Nasional,
Kajang 43000,
Selangor, Malaysia
e-mail: jagadeesh@uniten.edu.my

Tamer Khatib

Department of Energy Engineering
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,
Alpen-Adria-Universität Klagenfurt,
Klagenfurt 9020, Austria
e-mail: wilfried.elmenreich@aau.at

Hussein A. Kazem

Faculty of Engineering,
Sohar University,
Sohar PCI 311, Oman
e-mail: h.kazem@soharuni.edu.om

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 137(4), 041010 (Aug 01, 2015) (5 pages) Paper No: SOL-14-1300; doi: 10.1115/1.4030693 History: Received October 14, 2014; Revised May 22, 2015; Online June 04, 2015

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.

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Li, Y., Su, Y., and Shu, L., 2014, “An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System,” Renewable Energy, 66, pp. 78–89. [CrossRef]
Woyte, A., Van Thong, V., Belmans, R., and Nijs, J., 2006, “Voltage Fluctuations on Distribution Level Introduced by Photovoltaic Systems,” IEEE Trans. Energy Conv., 21(1), pp. 202–209. [CrossRef]
Mohammed, A., Pasupuleti, J., Khatib, T., and Elmenreich, W., 2015, “A Review of Process and Operational System Control of Hybrid Photovoltaic/Diesel Generator Systems,” Renewable Sustainable Energy Rev., 44, pp. 436–446. [CrossRef]
Dalton, G., Lockington, D., and Baldock, T., 2009, “Feasibility Analysis of Renewable Energy Supply Options for a Grid-Connected Large Hotel,” Renewable Energy, 34(4), pp. 955–964. [CrossRef]
Khatib, T., Mohamed, A., and Sopian, K., 2012, “A Software Tool for Optimal Sizing of PV Systems in Malaysia,” Model. Simul. Eng., 2012, p. 969248.
Picault, D., Raison, B., Bacha, S., De La Casa, J., and Aguilera, J., 2010, “Forecasting Photovoltaic Array Power Production Subject to Mismatch Losses,” Sol. Energy, 84(7), pp. 1301–1309. [CrossRef]
Su, Y., Chan, L.-C., Shu, L., and Tsui, K.-L., 2012, “Real-Time Prediction Models for Output Power and Efficiency of Grid-Connected Solar Photovoltaic Systems,” Appl. Energy, 93, pp. 319–326. [CrossRef]
Sulaiman, S. I., Rahman, T. K. A., Musirin, I., and Shaari, S., 2012, “An Artificial Immune-Based Hybrid Multi-Layer Feedforward Neural Network for Predicting Grid-Connected Photovoltaic System Output,” Energy Procedia, 14, pp. 260–264. [CrossRef]
Mellit, A., and Kalogirou, S. A., 2011, “ANFIS-Based Modelling for Photovoltaic Power Supply System: A Case Study,” Renewable Energy, 36(1), pp. 250–258. [CrossRef]
Wong, S., Wan, K. K., and Lam, T. N., 2010, “Artificial Neural Networks for Energy Analysis of Office Buildings With Daylighting,” Appl. Energy, 87(2), pp. 551–557. [CrossRef]
Kazem, H. A., Khatib, T., and Alwaeli, A. A., 2013, “Optimization of Photovoltaic Modules Tilt Angle for Oman,” 7th International Power Engineering and Optimization Conference (PEOCO), pp. 703–707.
Khatib, T., and Elmenreich, W., 2014, “Novel Simplified Hourly Energy Flow Models for Photovoltaic Power Systems,” Energy Conv. Manage., 79, pp. 441–448. [CrossRef]
Khatib, T., Mohamed, A., Sopian, K., and Mahmoud, M., 2012, “Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction,” Int. J. Photoenergy, 2012, p. 946890.
Geman, S., Bienenstock, E., and Doursat, R., 1992, “Neural Networks and the Bias/Variance Dilemma,” Neural Comput., 4(1), pp. 1–58. [CrossRef]
Blum, A., 1992, Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems, Wiley, New York.
Swingler, K., 1996, Applying Neural Networks: A Practical Guide, Morgan Kaufmann, New York.
Berry, M. J., and Linoff, G., 1997, Data Mining Techniques: for Marketing, Sales, and Customer Support, Wiley, New York.
Boger, Z., and Guterman, H., 1997, “Knowledge Extraction From Artificial Neural Network Models,” 1997 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3030–3035.
Caudill, M., and Butler, C., 1994, Understanding Neural Networks: Computer Explorations: A Workbook in Two Volumes With Software for the Macintosh and PC Compatibles,” MIT Press, Cambridge, MA. [PubMed] [PubMed]


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Fig. 1

Capture for the PV system used in this research

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Fig. 2

A schematic diagram of the basic architecture of CFNN

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Fig. 3

Output current prediction using CFNN

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Fig. 4

Output current prediction using CFNN, empirical, and regression model

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Fig. 5

Prediction the output current for a fuzzy weather day




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