Neural Computing and Applications

, Volume 31, Issue 12, pp 8985–8995 | Cite as

Artificial neural network approaches for modeling absorption spectrum of nanowire solar cells

  • Samaneh HamediEmail author
  • Zoheir Kordrostami
  • Ali Yadollahi
Original Article


The absorption spectrum of the silicon nanowire solar cells has been modeled using artificial neural networks. Multi-layer perceptrons (MLP) have been proposed as a precise and promising neural network to model the absorption of the nanowire solar cells with a very high accuracy. The MLP results have also been compared to the radial basis function (RBF) method. The proposed algorithm could successfully model the effect of the geometrical parameters of the nanowire array such as the nanowire diameter and pitch on the absorption spectrum. Finite difference time domain calculations show that the MLP network has tracked the sharp variations of the absorption spectrum more precisely. An optimum number of the neurons and epochs for MLP network have been obtained (8 neurons and 100 epochs). The results show that the optimized MLP network achieved a lower consuming time and error than RBF network. The calculated mean square error for the MLP network has been 0.0003 which verifies the high efficiency and accuracy of the proposed neural network model.


Solar cell Absorption Neural networks Nanowires Mean square error 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Dhas CR, Christy AJ, Venkatesh R, Panda SK, Subramanian B, Ravichandran K, Sudhagar P, Raj AME (2018) Low-cost and eco-friendly nebulizer spray coated CuInAlS2 counter electrode for dye-sensitized solar cells. Phys B 537:23–32. CrossRefGoogle Scholar
  2. 2.
    Wu J, Li Y, Tang Q, Yue G, Lin J, Huang M, Meng L (2014) Bifacial dye-sensitized solar cells: strategy to enhance overall efficiency based on transparent polyaniline electrode. Sci Rep 4:4028. CrossRefGoogle Scholar
  3. 3.
    Yoshikawa K, Kawasaki H, Yoshida W, Irie T, Konishi K, Nakano K, Uto T, Adachi D, Kanematsu M, Uzu H, Yamamoto K (2017) Silicon heterojunction solar cell with interdigitated back contacts for a photoconversion efficiency over 26%. Nat Energy 2:17032CrossRefGoogle Scholar
  4. 4.
    Chen G, Ning Z, Agren H (2016) Nanostructured solar cells. Nanomaterials (Basel) 6:145. CrossRefGoogle Scholar
  5. 5.
    Haverkort JE, Garnett EC, Bakkers EP (2018) Fundamentals of the nanowire solar cell: optimization of the open circuit voltage. Appl Phys Rev 5(3):031106. CrossRefGoogle Scholar
  6. 6.
    Abdellatif S, Kirah K, Ghannam R, Khalil ASG, Anis W (2018) Comprehensive study of various light trapping techniques used for sandwiched thin film solar cell structures. In: Physics, simulation, and photonic engineering of photovoltaic devices VII, vol 10527, p 1052715.
  7. 7.
    Kaya M, Hajimirza S (2018) Application of artificial neural network for accelerated optimization of ultra thin organic solar cells. Sol Energy 165:159–166. CrossRefGoogle Scholar
  8. 8.
    Lundgren C, Lopez R, Redwing J, Melde K (2013) FDTD modeling of solar energy absorption in silicon branched nanowires. Opt Express 21:A392–A400.C. CrossRefGoogle Scholar
  9. 9.
    French J, Mawdsley R, Fujiyama T, Achuthan K (2017) Artificial neural network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning. In: EGU general assembly conference, vol 19, p 15018.
  10. 10.
    Tev GJP, Faye MÉ, Moustapha SENE, Issa FAYE, Blieske U, Maiga AS (2018) solar photovoltaic panels failures causing power losses: a review. In: 2018 7th international energy and sustainability conference (IESC), pp 1–9.
  11. 11.
    Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Riess C (2018) Automatic classification of defective photovoltaic module cells in electroluminescence images. arXiv preprint arXiv:1807.02894
  12. 12.
    Sun TH, Tien FC, Tien FC, Kuo RJ (2016) Automated thermal fuse inspection using machine vision and artificial neural networks. J Intell Manuf 27:639–651CrossRefGoogle Scholar
  13. 13.
    Demuth HB, Beale MH, De Jess O, Hagan MT (2014) Neural network design. Martin Hagan, StillwaterGoogle Scholar
  14. 14.
    Kumar R, Aggarwal RK, Sharma JD (2015) Comparison of regression and artificial neural network models for estimation of global solar radiations. Renew Sustain Energy Rev 52:1294–1299. CrossRefGoogle Scholar
  15. 15.
    Kaya M, Hajimirza S (2018) Rapid optimization of external quantum efficiency of thin film solar cells using surrogate modeling of absorptivity. Sci Rep 8:8170CrossRefGoogle Scholar
  16. 16.
    Shen W, Huang F, Zhang X, Zhu Y, Chen X, Akbarjon N (2018) On-line chemical oxygen demand estimation models for the photoelectrocatalytic oxidation advanced treatment of papermaking wastewater. Water Sci Technol 78:310–319. CrossRefGoogle Scholar
  17. 17.
    Gurney K (2014) An introduction to neural networks. CRC Press, Boca RatonCrossRefGoogle Scholar
  18. 18.
    Samarasinghe S (2016) Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach Publications, New YorkzbMATHGoogle Scholar
  19. 19.
    Cilimkovic M (2015) Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15Google Scholar
  20. 20.
    Al-Amoudi A, Zhang L (2000) Application of radial basis function networks for solar-array modelling and maximum power-point prediction. IEE Proc Gener Trans Distrib 147(5):310–316. CrossRefGoogle Scholar
  21. 21.
    Chuang CC, Jeng JT, Lin PT (2004) Annealing robust radial basis function networks for function approximation with outliers. Neurocomputing 56:123–139. CrossRefGoogle Scholar
  22. 22.
    Zhao JY, Guo H, Li XN (2014) Research on algorithm optimization of hidden units data centre of RBF neural network. In: Advanced materials research, vol 831. Trans Tech Publications, pp 486–489. CrossRefGoogle Scholar
  23. 23.
    Punitha K, Devaraj D, Sakthivel S (2013) Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 62:330–340. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Samaneh Hamedi
    • 1
    Email author
  • Zoheir Kordrostami
    • 1
  • Ali Yadollahi
    • 1
  1. 1.Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran

Personalised recommendations