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International Journal of Photoenergy
Volume 2014, Article ID 193083, 12 pages
http://dx.doi.org/10.1155/2014/193083
Research Article

Artificial Neural Networks to Predict the Power Output of a PV Panel

DEIM Università degli studi di Palermo, Viale Delle Scienze, Edificio 9, 90128 Palermo, Italy

Received 28 May 2013; Accepted 29 November 2013; Published 23 January 2014

Academic Editor: David Worrall

Copyright © 2014 Valerio Lo Brano et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. Vossos, K. Garbesi, and H. Shen, “Energy savings from direct-DC in US residential buildings,” Energy and Buildings, vol. 68, pp. 223–231, 2014. View at Google Scholar
  2. W. D. Thomas and J. J. Duffy, “Energy performance of net-zero and near net-zero energy homes in New England,” Energy and Buildings, vol. 67, pp. 551–558, 2013. View at Google Scholar
  3. M. Cellura, L. Campanella, G. Ciulla et al., “The redesign of an Italian building to reach net zero energy performances: a case study of the SHC Task 40—ECBCS Annex 52,” in Proceedings of the ASHRAE Transactions, vol. 117, part 2, pp. 331–339, June 2011. View at Scopus
  4. J. G. Kang, J. H. Kim, and J. T. Kim, “Performance evaluation of DSC windows for buildings,” International Journal of Photoenergy, vol. 2013, Article ID 472086, 6 pages, 2013. View at Publisher · View at Google Scholar
  5. F. Asdrubali, F. Cotana, and A. Messineo, “On the evaluation of solar greenhouse efficiency in building simulation during the heating period,” Energies, vol. 5, no. 6, pp. 1864–1880, 2012. View at Google Scholar
  6. C. Rodriguez and G. A. J. Amaratunga, “Dynamic stability of grid-connected photovoltaic systems,” in Proceedings of the IEEE Power Engineering Society General Meeting, pp. 2193–2199, June 2004. View at Scopus
  7. L. Wang and Y.-H. Lin, “Random fluctuations on dynamic stability of a grid-connected photovoltaic array,” in Proceedings of the IEEE Power Engineering Society Winter Meeting, vol. 3, pp. 985–989, February 2001. View at Scopus
  8. Y. T. Tan and D. S. Kirschen, “Impact on the power system of a large penetration of photovoltaic generation,” in Proceedings of the IEEE Power Engineering Society General Meeting, pp. 1–8, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Skoplaki and J. A. Palyvos, “On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations,” Solar Energy, vol. 83, no. 5, pp. 614–624, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. V. Salas, E. Olías, A. Barrado, and A. Lázaro, “Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems,” Solar Energy Materials and Solar Cells, vol. 90, no. 11, pp. 1555–1578, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439–449, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Surya Kumari and C. Sai Babu, “Comparison of maximum power point tracking algorithms for photovoltaic system,” International Journal of Advances in Engineering and Technology, vol. 1, no. 5, pp. 133–148, 1963. View at Google Scholar
  13. M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs, “Theoretical and experimental analyses of photovoltaic systems with voltage- and current-based maximum power-point tracking,” IEEE Transactions on Energy Conversion, vol. 17, no. 4, pp. 514–522, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Ahmad and H.-J. Kim, “A voltage based maximum power point tracker for low power and low cost photovoltaic applications,” World Academy of Science, Engineering and Technology, vol. 60, pp. 714–717, 2009. View at Google Scholar · View at Scopus
  15. V. Lo Brano and G. Ciulla, “An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data,” Applied Energy, vol. 111, pp. 894–903, 2013. View at Google Scholar
  16. M. Veerachary, T. Senjyu, and K. Uezato, “Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller,” IEEE Transactions on Industrial Electronics, vol. 50, no. 4, pp. 749–758, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. B. M. Wilamowski and J. Binfet, “Microprocessor implementation of fuzzy systems and neural networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '01), vol. 1, pp. 234–239, Washington, DC, USA, July 2001. View at Scopus
  18. C.-Y. Won, D.-H. Kim, S.-C. Kim, W.-S. Kim, and H.-S. Kim, “New maximum power point tracker of photovoltaic arrays using fuzzy controller,” in Proceedings of th 25th Annual IEEE Power Electronics Specialists Conference (PESC '94), vol. 1, pp. 396–403, June 1994. View at Scopus
  19. A. E.-S. A. Nafeh, F. H. Fahmy, and E. M. Abou El-Zahab, “Evaluation of a proper controller performance for maximum-power point tracking of a stand-alone PV system,” Solar Energy Materials and Solar Cells, vol. 75, no. 3-4, pp. 723–728, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Patcharaprakiti, S. Premrudeepreechacharn, and Y. Sriuthaisiriwong, “Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system,” Renewable Energy, vol. 30, no. 11, pp. 1771–1788, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Hiyama, S. Kouzuma, and T. Imakubo, “Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control,” IEEE Transactions on Energy Conversion, vol. 10, no. 2, pp. 360–367, 1995. View at Publisher · View at Google Scholar · View at Scopus
  22. T. Hiyama, S. Kouzuma, T. Imakubo, and T. H. Ortmeyer, “Evaluation of neural network based real time maximum power tracking controller for PV system,” IEEE Transactions on Energy Conversion, vol. 10, no. 3, pp. 543–548, 1995. View at Google Scholar
  23. T. Hiyama and K. Kitabayashi, “Neural network based estimation of maximum power generation from PV module using environmental information,” IEEE Transactions on Energy Conversion, vol. 12, no. 3, pp. 241–246, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Cocconi and W. Rippel, “Lectures from GM sunracer case history, lecture 3-1: the Sunracer power systems,” Number M-101, Society of Automotive Engineers, Warderendale, Pa, USA, 1990. View at Google Scholar
  25. G. Ciulla, V. Lo Brano, and E. Moreci, “Forecasting the cell temperature of PV modules with an adaptive system,” International Journal of Photoenergy, vol. 2013, Article ID 192854, 10 pages, 2013. View at Publisher · View at Google Scholar
  26. V. Lo Brano, G. Ciulla, and M. Beccali, “Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel,” in Proceedings of the International Conferences on Computational Science and Its Applications (ICCSA '13), pp. 344–358, Springer, Ho Chi Minh City, Vietnam, 2013.
  27. K. S. Yigit and H. M. Ertunc, “Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks,” International Communications in Heat and Mass Transfer, vol. 33, no. 7, pp. 898–907, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. M. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design, PWS Publishing Company, Boston, Mass, USA, 1995.
  29. S. Danaher, S. Datta, I. Waddle, and P. Hackney, “Erosion modelling using Bayesian regulated artificial neural networks,” Wear, vol. 256, no. 9-10, pp. 879–888, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Haykin, Neural Networks: A Comprehensive Foundation, MacMillan, New York, NY, USA, 1994.
  31. V. Pacelli and M. Azzollini, “An artificial neural network approach for credit risk management,” Journal of Intelligent Learning Systems and Applications, vol. 3, no. 2, pp. 103–112, 2011. View at Google Scholar
  32. E. Angelini, G. di Tollo, and A. Roli, “A neural network approach for credit risk evaluation,” Quarterly Review of Economics and Finance, vol. 48, no. 4, pp. 733–755, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. V. Lo Brano, A. Orioli, G. Ciulla, and S. Culotta, “Quality of wind speed fitting distributions for the urban area of Palermo, Italy,” Renewable Energy, vol. 36, no. 3, pp. 1026–1039, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. V. Lo Brano, A. Orioli, and G. Ciulla, “On the experimental validation of an improved five-parameter model for silicon photovoltaic modules,” Solar Energy Materials and Solar Cells, vol. 105, pp. 27–39, 2012. View at Google Scholar
  35. V. Lo Brano, A. Orioli, G. Ciulla, and A. di Gangi, “An improved five-parameter model for photovoltaic modules,” Solar Energy Materials and Solar Cells, vol. 94, no. 8, pp. 1358–1370, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. J. C. Principe, N. R. Euliano, and W. C. Lefebvre, Neural and Adaptive Systems: Fundamentals Through Simulations, John Wiley & Sons, New York, NY, USA, 1999.