In this paper, artificial neural networks (ANNs) have been used for the performance ratio modelling of four photovoltaic (PV) modules. The PV modules are selected from three different silicon technologies including one monocrystalline, two polycrystalline, and one micromorph (a-Si/μc-Si) modules. The adopted ANN architecture is a multilayer perceptron (MLP). The inputs of the ANN models are the solar irradiance on the PV module plane and air ambient temperature, while the output is the PV module performance ratio. It is shown that ANN models with three layers and five hidden neurons accurately model the performance ratio regardless of PV module technology. The results obtained from the ANN model are compared with those obtained from the five parameter model (L5P). The model comparison is done through two widely used forecasting errors: the root mean square error (RMSE) and the mean absolute percentage of error (MAPE). The values of both RMSE and MAPE are less than 0.02 for MLP based models and are about three to nine times lower than those obtained from the electrical model. It is also shown that the poor fit of the L5P model is due to the bad estimation of series and shunt resistances.
References
1.
Almonacid
, F.
, Pérez-Higueras
, P. J.
, Fernández
, E. F.
, and Hontoria
, L.
, “A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator
,” Energy Convers. Manage.
85
, 389
–398
(2014
).2.
Almonacid
, F.
, Rus
, C.
, Pérez-Higueras
, P.
, and Hontoria
, L.
, “Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks
,” Energy
36
, 375
–384
(2011
).3.
Alonso García
, M. C.
and Balenzategui
, J. L.
, “Estimation of photovoltaic module yearly temperature and performance based on nominal operation cell temperature calculations
,” Renewable Energy
29
, 1997
–2010
(2004
).4.
Amrouche
, B.
, Sicot
, L.
, Guessoum
, A.
, and Belhamel
, M.
, “Experimental analysis of the maximum power point's properties for four photovoltaic modules from different technologies: Monocrystalline and polycrystalline silicon, CIS and CdTe
,” Sol. Energy Mater. Sol. Cells
118
, 124
–134
(2013
).5.
Aste
, N.
, Del Pero
, C.
, and Leonforte
, F.
, “PV technologies performance comparison in temperate climates
,” Sol. Energy
109
, 1
–10
(2014
).6.
Avrutin
, V.
, Izyumskaya
, N.
, and Morkoç
, H.
, “Amorphous and micromorph Si solar cells: Current status and outlook
,” Turk. J. Phys.
38
, 526
–542
(2014
).7.
Bai
, J.
, Liu
, S.
, Hao
, Y.
, Zhang
, Z.
, Jiang
, M.
, and Zhang
, Y.
, “Development of a new compound method to extract the five parameters of PV modules
,” Energy Convers. Manage.
79
, 294
–303
(2014
).8.
Baroughi
, M. F.
, Wang
, J.
, and Shanmugam
, M.
, “Micromorph Si-multicrystalline Si tandem solar cells for cost effective photovoltaics
,” in 33rd IEEE Photovoltaic Specialists Conference, PVSC'08, IEEE
(2008
), pp. 1
–4
.9.
Basheer
, I. A.
and Hajmeer
, M.
, “Artificial neural networks: Fundamentals, computing, design, and application
,” J. Microbiol. Methods
43
, 3
–31
(2000
).10.
Bonkoungou
, D.
, Koalaga
, Z.
, and Njomo
, D.
, “Modelling and simulation of photovoltaic module considering single-diode equivalent circuit model in MATLAB
,” Int. J. Emerg. Technol. Adv. Eng.
3
, 493
–502
(2013
).11.
Bücher
, K.
, “Site dependence of the energy collection of PV modules
,” Sol. Energy Mater. Sol. Cells
47
, 85
–94
(1997
).12.
Cañete
, C.
, Carretero
, J.
, and Sidrach-de-Cardona
, M.
, “Energy performance of different photovoltaic module technologies under outdoor conditions
,” Energy
65
, 295
–302
(2014
).13.
Celik
, A. N.
, “Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules
,” Sol. Energy
85
, 2507
–2517
(2011
).14.
Ceylan
, İ.
, Erkaymaz
, O.
, Gedik
, E.
, and Gürel
, A. E.
, “The prediction of photovoltaic module temperature with artificial neural networks
,” Case Stud. Therm. Eng.
3
, 11
–20
(2014a
).15.
Ceylan
, İ.
, Gedik
, E.
, Erkaymaz
, O.
, and Gürel
, A. E.
, “The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey
,” Energy Build.
84
, 258
–267
(2014b
).16.
Chan
, D. S. H.
, Phillips
, J. R.
, and Phang
, J. C. H.
, “A comparative study of extraction methods for solar cell model parameters
,” Solid-State Electron.
29
, 329
–337
(1986
).17.
Cornaro
, C.
and Musella
, D.
, “Performance analysis of PV modules of various technologies after more than one year of outdoor exposure in Rome
,” in Proceedings of the III International Conference on Applied Energy
(2011
).18.
De Blas
, M. A.
, Torres
, J. L.
, Prieto
, E.
, and Garcıa
, A.
, “Selecting a suitable model for characterizing photovoltaic devices
,” Renewable Energy
25
, 371
–380
(2002
).19.
De Soto
, W.
, Klein
, S. A.
, and Beckman
, W. A.
, “Improvement and validation of a model for photovoltaic array performance
,” Sol. Energy
80
, 78
–88
(2006
).20.
Demuth
, H.
, Beale
, M.
, and Hagan
, M.
, Neural Network ToolboxTM 6 User's guide. Mathworks Inc.
(2008
).21.
Gardner
, M. W.
and Dorling
, S. R.
, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
,” Atmos. Environ.
32
, 2627
–2636
(1998
).22.
Guérin de Montgareuil
, A.
, Sicot
, L.
, Martin
, J.-L.
, Mezzasalma
, F.
, Delesse
, Y.
, and Merten
, J.
, “A new tool for the MotherPV method: Modeling of the Irradiance Coefficient of Photovoltaic Modules
,” in 24th European Photovoltaic Solar Energy Conference
, 21–25 September 2009, Hamburg
, Germany
(2009
), pp. 3305
–3309
.23.
Guérin de Montgareuil
, A.
, Sicot
, L.
, Mohring
, H.
, Stellbogen
, D.
, Zdanowicz
, T.
, Prorok
, M.
, Kołodenny
, W.
, Martin
, J.-L.
, Mezzasalma
, F.
, Delesse
, Y.
, and Merten
, J.
, “First results of the application of the MotherPV method to CIS modules
,” in 24th European Photovoltaic Solar Energy Conference
, 21-25 September 2009, Hamburg, Germany (2009
), pp. 3451–3455.24.
Kamkird
, P.
, Ketjoy
, N.
, Rakwichian
, W.
, and Sukchai
, S.
, “Investigation on temperature coefficients of three types photovoltaic module technologies under Thailand operating condition
,” Procedia Eng.
32
, 376
–383
(2012
).25.
Karamirad
, M.
, Omid
, M.
, Alimardani
, R.
, Mousazadeh
, H.
, and Heidari
, S. N.
, “ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules
,” Simul. Model. Pract. Theory
34
, 86
–98
(2013
).26.
Karsoliya
, S.
, “Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture
,” Int. J. Eng. Trends Technol.
3
, 713
–717
(2012
).27.
Kurnik
, J.
, Jankovec
, M.
, Brecl
, K.
, and Topic
, M.
, “Outdoor testing of PV module temperature and performance under different mounting and operational conditions
,” Sol. Energy Mater. Sol. Cells
95
, 373
–376
(2011
).28.
Ma
, T.
, Yang
, H.
, and Lu
, L.
, “Development of a model to simulate the performance characteristics of crystalline silicon photovoltaic modules/strings/arrays
,” Sol. Energy
100
, 31
–41
(2014
).29.
Makrides
, G.
, Zinsser
, B.
, Georghiou
, G. E.
, Schubert
, M.
, and Werner
, J. H.
, “Temperature behaviour of different photovoltaic systems installed in Cyprus and Germany
,” Sol. Energy Mater. Sol. Cells
93
, 1095
–1099
(2009
).30.
Makrides
, G.
, Zinsser
, B.
, Georghiou
, G. E.
, Schubert
, M.
, and Werner
, J. H.
, “Outdoor efficiency of different photovoltaic systems installed in Cyprus and Germany
,” in 33rd IEEE Photovoltaic Specialists Conference, 2008
. PVSC'08, IEEE (2008
), pp. 1
–6
.31.
McMahon
, T. J.
, Basso
, T. S.
, and Rummel
, S. R.
, “Cell shunt resistance and photovoltaic module performance
,” in Conference Record of the Twenty Fifth IEEE Photovoltaic Specialists Conference, 1996,
IEEE (1996
), pp. 1291
–1294
.32.
Mellit
, A.
, Sağlam
, S.
, and Kalogirou
, S. A.
, “Artificial neural network-based model for estimating the produced power of a photovoltaic module
,” Renewable Energy
60
, 71
–78
(2013
).33.
Merten
, J.
, Sicot
, L.
, Delesse
, Y.
, and de Montgareuil
, A. G.
, “Outdoor evaluation of the energy production of different module technologies
,” in Proceeding of the 23rd European Photovoltaic Solar Energy Conference Exhibition
(2008
), pp. 2841
–2845
.34.
Merten
, J.
, Sicot
, L.
, Delesse
, Y.
, Martin
, J.-L.
, Mezzasalma
, F.
, and Guérin de Montgareuil
, A.
, “In situ monitoring of degradation processes inside PV modules of different technologies
,” in 24th European Photovoltaic Solar Energy Conference
, 21–25 September 2009, Hamburg, Germany (2009
), pp. 3490
–3493
.35.
Merten
, J.
, Sicot
, L.
, Delesse
, Y.
, and Montgareuil
, A. G.
, “Outdoor evaluation of the energy production of different module technologies
,” in Proceedings of the 22nd European Photovoltaic Solar Energy Conference, Eds2008
(2008
).36.
Mosalam Shaltout
, M. A.
, El-Hadad
, A. A.
, Fadly
, M. A.
, Hassan
, A. F.
, and Mahrous
, A. M.
, “Determination of suitable types of solar cells for optimal outdoor performance in desert climate
,” Renewable Energy
19
, 71
–74
(2000
).37.
Ndiaye
, A.
, Kébé
, C. M. F.
, Charki
, A.
, Ndiaye
, P. A.
, Sambou
, V.
, and Kobi
, A.
, “Degradation evaluation of crystalline-silicon photovoltaic modules after a few operation years in a tropical environment
,” Sol. Energy
103
, 70
–77
(2014
).38.
Phinikarides
, A.
, Kindyni
, N.
, Makrides
, G.
, and Georghiou
, G. E.
, “Review of photovoltaic degradation rate methodologies
,” Renewable Sustainable Energy Rev.
40
, 143
–152
(2014
).39.
Piliougine
, M.
, Carretero
, J.
, Mora-López
, L.
, and Sidrach-de-Cardona
, M.
, “Experimental system for current-voltage curve measurement of photovoltaic modules under outdoor conditions
,” Prog. Photovoltaics Res. Appl.
19
, 591
–602
(2011
).40.
Rüther
, R.
, Kleiss
, G.
, and Reiche
, K.
, “Spectral effects on amorphous silicon solar module fill factors
,” Sol. Energy Mater. Sol. Cells
71
, 375
–385
(2002
).41.
Sharma
, V.
, Kumar
, A.
, Sastry
, O. S.
, and Chandel
, S. S.
, “Performance assessment of different solar photovoltaic technologies under similar outdoor conditions
,” Energy
58
, 511
–518
(2013
).42.
Sicot
, L.
, Razongles
, G.
, Merten
, J.
, Mezzasalma
, F.
, Martin
, J.-L.
, Favre
, W.
, Delesse
, Y.
, and Guérin de Montgareuil
, A.
, “From watt-peak to watt-hours: MotherPV method and IEC 61853 standard
,” in 28th European Photovoltaic Solar Energy Conference Exhibition
(2013
), pp. 2935
–2938
.43.
Skoplaki
, E.
and Palyvos
, J. A.
, “On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlations
,” Sol. Energy
83
, 614
–624
(2009
).44.
Tossa
, A. K.
, Soro
, Y. M.
, Azoumah
, Y.
, and Yamegueu
, D.
, “A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions
,” Sol. Energy
110
, 543
–560
(2014
).45.
Tossa
, A. K.
, Soro
, Y. M.
, Thiaw
, L.
, Azoumah
, Y.
, Sicot
, L.
, Yamegueu
, D.
, Lishou
, C.
, Coulibaly
, Y.
, and Razongles
, G.
, “Energy performance of different silicon photovoltaic technologies under hot and harsh climate,” Energy
, 103
, 261
–270
(2016
).46.
Van Dyk
, E. E.
and Meyer
, E. L.
, “Analysis of the effect of parasitic resistances on the performance of photovoltaic modules
,” Renewable Energy
29
, 333
–344
(2004
).47.
Velilla
, E.
, Valencia
, J.
, and Jaramillo
, F.
, “Performance evaluation of two solar photovoltaic technologies under atmospheric exposure using artificial neural network models
,” Sol. Energy
107
, 260
–271
(2014
).48.
World Nuclear Association
, http://www.world-nuclear.org/information-library/energy-and-the-environment/renewable-energy-and-electricity.aspx for “Renewable Energy and Electricity” (accessed 3.2.2017
).49.
Yap
, W. K.
and Karri
, V.
, “An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques
,” Renewable Energy
78
, 42
–50
(2015
).© 2018 Author(s).
2018
Author(s)
You do not currently have access to this content.
