Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  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. https://doi.org/10.1016/j.physb.2018.01.042

    Article  Google Scholar 

  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. https://doi.org/10.1038/srep04028

    Article  Google Scholar 

  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 2:17032

    Article  Google Scholar 

  4. Chen G, Ning Z, Agren H (2016) Nanostructured solar cells. Nanomaterials (Basel) 6:145. https://doi.org/10.3390/nano6080145

    Article  Google Scholar 

  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. https://doi.org/10.1063/1.5028049

    Article  Google Scholar 

  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. https://doi.org/10.1117/12.2291613

  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. https://doi.org/10.1016/j.solener.2018.02.062

    Article  Google Scholar 

  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. https://doi.org/10.1364/OE.21.00A392

    Article  Google Scholar 

  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. https://doi.org/10.1109/iesc.2018.8439986

  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. https://doi.org/10.1109/iesc.2018.8439986

  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. Sun TH, Tien FC, Tien FC, Kuo RJ (2016) Automated thermal fuse inspection using machine vision and artificial neural networks. J Intell Manuf 27:639–651

    Article  Google Scholar 

  13. Demuth HB, Beale MH, De Jess O, Hagan MT (2014) Neural network design. Martin Hagan, Stillwater

    Google Scholar 

  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. https://doi.org/10.1016/j.rser.2015.08.021

    Article  Google Scholar 

  15. Kaya M, Hajimirza S (2018) Rapid optimization of external quantum efficiency of thin film solar cells using surrogate modeling of absorptivity. Sci Rep 8:8170

    Article  Google Scholar 

  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. https://doi.org/10.2166/wst.2018.299

    Article  Google Scholar 

  17. Gurney K (2014) An introduction to neural networks. CRC Press, Boca Raton

    Book  Google Scholar 

  18. Samarasinghe S (2016) Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach Publications, New York

    MATH  Google Scholar 

  19. Cilimkovic M (2015) Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15

  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. https://doi.org/10.1049/ip-gtd:20000605

    Article  Google Scholar 

  21. Chuang CC, Jeng JT, Lin PT (2004) Annealing robust radial basis function networks for function approximation with outliers. Neurocomputing 56:123–139. https://doi.org/10.1016/S0925-2312(03)00436-3

    Article  Google Scholar 

  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. https://doi.org/10.4028/www.scientific.net/AMR.831.486

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.energy.2013.08.022

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran

    Samaneh Hamedi, Zoheir Kordrostami & Ali Yadollahi

Authors
  1. Samaneh Hamedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zoheir Kordrostami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Yadollahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samaneh Hamedi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamedi, S., Kordrostami, Z. & Yadollahi, A. Artificial neural network approaches for modeling absorption spectrum of nanowire solar cells. Neural Comput & Applic 31, 8985–8995 (2019). https://doi.org/10.1007/s00521-019-04406-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04406-3

Keywords

Search

Navigation