Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Simulated Performance of a Photovoltaic Module: A Comparison of ANN and Regression Based Models

Author : Sandhya Prajapati 1 E. Fernandez 2

PDF

Date of Publication :22nd February 2018

Abstract: In this paper, we attempt to compare the output prediction accuracy of two models – the ANN model and the Linear Regression model as applied to the case of the simulated performance of the PV module. The input variables for each model are Voc, Isc, Insolation level, Temperature while the output variable is Pmax. ANN models after trial show that the model with 4-10-1-1 configuration is suitable. The linear regression model is multiple regression models involving the same variables. The data base for this study is derived from various available manufacturers’ data, together with some experimental outputs from a few research reports. The results of this analysis appear to indicate that the ANN model is superior in prediction performance.

Reference :

    1. J. Bikaneria et al.,“Modeling and simulation of PV cell using one-diode model,” IJSRP, vol. 3, issue 10, 2013.
    2. M Azzouzi, D. Popescu and M Bouchahdane, “Modeling of electrical characteristics of photovoltaic cell considering a single diode”, Journal of clean energy technologies, Vol.4 (6), pp. 414-419, 2016.
    3. Mohd. Benghanem and S.N. Alamri, “Modeling of PV module and experimental determination of serial resistance”, Journal of Taibah University for Science, Vol 2, pp 94-105, 2009.
    4. E.M.G. Rodrigues , R. Melício, V.M.F. Mendes and J.P.S. Catalão., “Simulation of a solar cell considering single-diode equivalent circuit model,” in Proc. International Conference on Renewable Energies and Power Quality, 2011.
    5. Wook Kim, Woojin Choi “A novel parameter extraction method for the one-diode solar cell model” Solar Energy 84, pp.1008–1019, 2010. [6] B.Alsayid,“Modeling and simulation of photovoltaic cell/module/array with two-diode model,” IJCTEE, vol. 1, no. 3, 2012.
    6. Gow J.A, Manning C. D.,“Development of a photovoltaic array model for use in power electronics simulation studies,IEE Proc Electrical Power Applications, pp.146:193–200,1999
    7. Tanvir Ahmad, Sharmin Sobhan, Mohd. Faysal Nayan, “ Comparative Analysis between Single Diode and Double Diode Model of PV Cell: Concentrate Different Parameters Effect on Its Efficiency”, Journal of Power and Energy Engineering, Vol. 4, pp.31-46, 2016.
    8. Yadir, S., Assal, S., El Rhassouli, A., Sidki, M., Benhmida, M., "A new technique for extracting physical parameters of a solar cell model from the double exponential model (DECM)", Optical Materials vol. 36, pp. 18-21, Nov. 2013.
    9. Z.Ouennoughi, and M. Cheggar, “A simpler method for extracting solar cell parameters using the conductance method,” Solid-State Electronics Vol.43, , pp. 1985- 1988, 1999.
    10. M.F. AlHajri, K.M. El-Naggar, M.R. AlRashidi, A.K. Al-Othman “Optimal extraction of solar cell parameters using pattern search” Renewable Energy 44 ,pp 238-245, 2012.
    11. M.R. AlRashidi , M.F. AlHajri, K.M. El-Naggar, A.K. Al-Othman “.A new estimation approach for determining the I–V characteristics of solar cells”, Solar Energy (85) pp.1543–1550, 2011
    12. H. Mekki, A. Mellit, H.Salhi, and K. Belhout, “Modeling and Simulation of Photovoltaic Panel based on Artificial Neural Networks and VHDL-Language’’, 4th International Conference on Computer Integrated Manufacturing CIP’2007, 03-04 November 2007
    13. F. Almonacid, C. Rus, L. Hontoria, F.J. Munoz, “Characterisation of PV CIS module by artificial neural networks:A comparative study with other methods”, Renewable Energy 35, pp. 973-980, 2010.
    14. S. A. Kalogirou, “Artificial neural-networks for energy systems”,Applied Energy, vol. 67, pp. 17 - 35, 2000.
    15. F. Bonanno, G. Capizzi, C. Napoli, G. Graditi, G. Marco Tina, “A radial basis function neural network approach for the electrical characteristics estimation of a photovoltaic module”,Applied Energy, vol. 97, pp. 956- 961, 2012.
    16. Alireza Askarzadeh, “Voltage prediction of a photovoltaic module using artificial neural networks”, International Transaction on Electrical Energy. Systems, (24), pp.715–1725, 2014
    17. M. Karamirad,M. Omid, R. Alimardani, H. Mousazadeh, S.N. Heidari “ANN based simulation and experimental verification of analytical four- and fiveparameters models of PV modules” Simulation Modelling Practice and Theory, 34, pp. 86-98, 2013.
    18. A. Mellit, S. SaÄŸlam, S.A. Kalogirou “Artificial neural network-based model for estimating the produced power of a photovoltaic module”, Renewable Energy, 60, pp. 71-78, 2013
    19. Moufdi Hadjab, Smail Berrah and Hamza Abid, “Neural network for modeling solar panel”, International Journal of Energy, Issue 1, Vol. 6, 2012.
    20. S. H. Oudjana, A. Hellal, and I. Hadj Mahammed, “Power Forecasting of Photovoltaic Generation”, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering ,Vol. 7, No 6, 2013
    21. Salim Moslehi, T. Agami Reddy, Srinivas Katipamula “Evaluation of data-driven models for predicting solar photovoltaics power output”, Energy Vol.142, pp 1057-1065, 2018(Available online 10 September 2017)
    22. A. M. Muzathik, “Photovoltaic Modules Operating Temperature Estimation Using a Simple Correlation”, International Journal of Energy Engineering,Vol. 4 (4) ,pp. 151-158, Aug. 2014
    23. T. Bhattacharya ,A. K. Chakraborty , and K. Pal, “Statistical Analysis of the Performance of Solar Photovoltaic Module with the Influence of Different Meteorological Parameters in Tripura, India”, International Journal of Engineering Research Volume No.4(3), pp 137 – 140, March 2015.
    24. Abhineet Samadhiya and Ruchi Pandey, “Analysis of PV Panels under Various Weather Conditions”, International Journal of Emerging Research in Management &Technology, Volume 5(2), February 2016.
    25. Saiful Anwar, Yoshiki Mikami, “Comparing Accuracy Performance of ANN,MLR, and GARCH Model in Predicting Time Deposit Return of Islamic Bank”, International Journal of Trade, Economics and Finance, Vol.2, No.1, February, 2011
    26. Vijay S. Desai and Rakesh Bharati, “A comparison of linear regression and neural networkmethods for predicting excess returns on large stocks” Annals of Operations Research Vol. 78, pp.127 – 163,1998
    27. Erdi Tosun , Kadir Aydin , Mehmet Bilgili, “Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodieselalcohol mixtures”, Alexandria Engineering Journal Vol. 55, pp.3081–3089,2016. [29] Damoder N Gujarati,“Basic Econometrics”, Fourth Edition ,Mc Graw-Hill Companies , 2004.

Recent Article