Skip to main content
Springer Nature Link
Log in

Characterisation and Modeling of Organic Solar Cells by Using Radial Basis Neural Networks

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

Included in the following conference series:

Abstract

Neural network architectures have been proven useful to model the intrinsic characteristics of photovoltaic cells. The possibility to get rid of an a priori model is one of the many advantages of such an approach as well as the resulting accuracy, robustness and speed. Neural networks have been used to model the characteristics of traditional silicon-based photovoltaic modules, and in this work we have investigated a model for new generation organic solar cells. Silicon-based cells were generally prone to be modeled by simple circuital parameter sets, however for organic cells the process is generally impervious. For this reason, we show that the application of Radial Basis Neural Networks has resulted advantageous to modeling. We have used such networks together with an algorithmic solution to automatically parametrize the Voltage-Current characteristics of organic photovoltaic modules.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. AbdulHadi, M., Al-Ibrahim, A.M., Virk, G.S.: Neuro-fuzzy-based solar cell model. IEEE Trans. Energy Convers. 19(3), 619–624 (2004)

    Article  Google Scholar 

  2. Bonanno, F., Capizzi, G., Napoli, C.: Hybrid neural networks architectures for soc and voltage prediction of new generation batteries storage. In: Proceedings of IEEE International Conference on Clean Electrical Power (ICCEP), Ischia, Italy, pp. 341–344, June 2011. doi:10.1109/ICCEP.2011.6036301

  3. Bonanno, F., Capizzi, G., Napoli, C.: Some remarks on the application of rnn and prnn for the charge-discharge simulation of advanced lithium-ions battery energy storage. In: Proceedings of IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, Italy, pp. 941–945, June 2012. doi:10.1109/SPEEDAM.2012.6264500

  4. Bonanno, F., Capizzi, G., Sciuto, G.L., Napoli, C., Pappalardo, G., Tramontana, E.: A cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 22–33. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07173-2_3

    Chapter  Google Scholar 

  5. Capizzi, G., Bonanno, F., Napoli, C.: Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources. In: IEEE International Conference on Clean Electrical Power (ICCEP), pp. 336–340. IEEE (2011)

    Google Scholar 

  6. Capizzi, G., Napoli, C., Paternò, L.: An innovative hybrid neuro-wavelet method for reconstruction of missing data in astronomical photometric surveys. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 21–29. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29347-4_3

    Chapter  Google Scholar 

  7. Di Piazza, M.C., Vitale, G.: Photovoltaic field emulation including dynamic and partial shadow conditions. Appl. Energy 87(3), 814–823 (2010)

    Article  Google Scholar 

  8. Dziwiński, P., Bartczuk, Ł., Przybył, A., Avedyan, E.D.: A new algorithm for identification of significant operating points using swarm intelligence. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 349–362. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  9. Horzyk, A.: Innovative types and abilities of neural networks based on associative mechanisms and a new associative model of neurons. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 26–38. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19324-3_3

    Chapter  Google Scholar 

  10. Huang, T., Li, C., Duan, S., Starzyk, J.: Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 866–875 (2012). doi:10.1109/TNNLS.2012.2192135

    Article  Google Scholar 

  11. Karayiannis, N.B., Mi, G.W.: Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans. Neural Netw. 8(6), 1492–1506 (1997)

    Article  Google Scholar 

  12. Napoli, C., Pappalardo, G., Tramontana, E., Nowicki, R.K., Starczewski, J.T., Woźniak, M.: Toward work groups classification based on probabilistic neural network approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 79–89. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19324-3_8

    Chapter  Google Scholar 

  13. Napoli, C., Pappalardo, G., Tramontana, E., Zappalà, G.: A cloud-distributed gpu architecture for pattern identification in segmented detectors big-data surveys. Comput. J. 59(3), 338–352 (2016). doi:10.1093/comjnl/bxu147

    Article  Google Scholar 

  14. Nguyen, V., Starzyk, J., Goh, W., Jachyra, D.: Neural network structure for spatio-temporal long-term memory. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 971–983 (2012). doi:10.1109/TNNLS.2012.2191419

    Article  Google Scholar 

  15. Nowak, B.A., Nowicki, R.K., Woźniak, M., Napoli, C.: Multi-class nearest neighbour classifier for incomplete data handling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 469–480. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19324-3_42

    Chapter  Google Scholar 

  16. Ravaee, H., Farahat, S., Sarhaddi, F.: Artificial neural network based model of photovoltaic thermal (pv/t) collector. J. Math. Comput. Sci. 4(3), 411–417 (2012)

    Google Scholar 

  17. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)

    Article  Google Scholar 

  18. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)

    Article  MathSciNet  Google Scholar 

  19. Sandrolini, L., Artioli, M., Reggiani, U.: Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl. Energy 87(2), 442–451 (2010)

    Article  Google Scholar 

  20. Singh, K.J., Kho, K.R., Singh, S.J., Devi, Y.C., Singh, N.B., Sarkar, S.: Artificial neural network approach for more accurate solar cell electrical circuit model. Int. J. Comput. Appl. 4(3), 101–116 (2014)

    Google Scholar 

  21. Starczewski, J.T., Nowicki, R.K., Nowak, B.A.: Genetic fuzzy classifier with fuzzy rough sets for imprecise data. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, China, pp. 1382–1389, 6–11 July 2014

    Google Scholar 

  22. Swiechowski, M., Mandziuk, J.: Self-adaptation of playing strategies in general game playing. IEEE Trans. Comput. Intell. AI Games 6(4), 367–381 (2014)

    Article  Google Scholar 

  23. Waledzik, K., Mandziuk, J.: An automatically generated evaluation function in general game playing. IEEE Trans. Comput. Intell. AI Games 6(3), 258–270 (2014)

    Article  Google Scholar 

  24. Wozniak, M., Polap, D., Nowicki, R.K., Napoli, C., Pappalardo, G., Tramontana, E.: Novel approach toward medical signals classifier. In: International Joint Conference on Neural Networks (IJCNN), pp. 1924–1930. IEEE (2015)

    Google Scholar 

Download references

Acknowledgments

This work has been supported by the BGU-ENEA joint lab and the ILSE-Joint Italian-Israeli Laboratory on Solar and Alternative Energies.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beersheba, Israel

    Dor Gotleyb & Rafi Shikler

  2. Department of Engineering, Roma Tre University, Rome, Italy

    Grazia Lo Sciuto

  3. Department of Mathematics and Informatics, University of Catania, Catania, Italy

    Christian Napoli & Emiliano Tramontana

  4. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100, Gliwice, Poland

    Marcin Woźniak

Authors
  1. Dor Gotleyb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Grazia Lo Sciuto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Napoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafi Shikler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emiliano Tramontana
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcin Woźniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Napoli .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Gotleyb, D., Sciuto, G.L., Napoli, C., Shikler, R., Tramontana, E., Woźniak, M. (2016). Characterisation and Modeling of Organic Solar Cells by Using Radial Basis Neural Networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39378-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics