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International Journal of Photoenergy
Volume 2014, Article ID 469701, 10 pages
http://dx.doi.org/10.1155/2014/469701
Research Article

Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia

Received 9 January 2014; Accepted 26 February 2014; Published 6 April 2014

Academic Editor: Niyaz Mohammad Mahmoodi

Copyright © 2014 Aminmohammad Saberian 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. L. Martín, L. F. Zarzalejo, J. Polo, A. Navarro, R. Marchante, and M. Cony, “Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning,” Solar Energy, vol. 84, no. 10, pp. 1772–1781, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Heinemann, E. Lorenz, and M. Girodo, “Forecasting of solar radiation,” in Solar Energy Resource Management for Electricity Generation From Local Level to Global Scale, Nova Science Publishers, New York, NY, USA, 2006. View at Google Scholar
  3. R. Perez, K. Moore, S. Wilcox, D. Renné, and A. Zelenka, “Forecasting solar radiation—preliminary evaluation of an approach based upon the national forecast database,” Solar Energy, vol. 81, no. 6, pp. 809–812, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Mellit and A. M. Pavan, “A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy,” Solar Energy, vol. 84, no. 5, pp. 807–821, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Wang, F. Wang, and S. Su, “Solar irradiance short-term prediction model based on BP neural network,” Energy Procedia, vol. 12, pp. 488–494, 2011. View at Google Scholar
  6. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989. View at Google Scholar · View at Scopus
  7. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Khatib, A. Mohamed, K. Sopian, and M. Mahmoud, “Assessment of artificial neural networks for hourly solar radiation prediction,” International Journal of Photoenergy, vol. 2012, Article ID 946890, 7 pages, 2012. View at Publisher · View at Google Scholar
  9. S. Krauter and T. Depping, “Monitoring of remote PV-systems via satellite,” in Proceddings of the 3rd World Conference on Photovoltaic Energy Conversion, pp. 2202–2205, May 2003. View at Scopus
  10. J. D. Mondol, Y. G. Yohanis, and B. Norton, “The effect of low insolation conditions and inverter oversizing on the long-term performance of a grid-connected photovoltaic system,” Progress in Photovoltaics: Research and Applications, vol. 15, no. 4, pp. 353–368, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. I. Morsy, A. Aboul Seoud, and A. El Zawawi, “On-line prediction of photovoltaic output power under cloudy skies by using fuzzy logic,” in Proceedings of the 19th National Radio Science Conference (NRSC '02), pp. 519–526.
  12. A. V. Timbus, R. Teodorescu, F. Blaabjerg, and U. Borup, “Online grid measurement and ENS detection for PV inverter running on highly inductive grid,” IEEE Power Electronics Letters, vol. 2, no. 3, pp. 77–82, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. E. E. van Dyk, E. L. Meyer, F. J. Vorster, and A. W. R. Leitch, “Long-term monitoring of photovoltaic devices,” Renewable Energy, vol. 25, no. 2, pp. 183–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Mantri, H. Mangalvedhekar, and P. Gupta, “Weather sensitive short term load forecasting using fully connected feed forward neural network,” International Journal of Engineering, vol. 2, 2013. View at Google Scholar
  15. K. A. Kumari, N. K. Boiroju, T. Ganesh, and P. R. Reddy, “Forecasting surface air temperature using neural networks,” International Journal of Mathematics and Computer Applications Research, vol. 3, pp. 65–78, 2012. View at Google Scholar
  16. A. Mellit and S. A. Kalogirou, “Artificial intelligence techniques for photovoltaic applications: a review,” Progress in Energy and Combustion Science, vol. 34, no. 5, pp. 574–632, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. M. A. AbdulAzeez, “Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau, Nigeria,” Archives of Applied Science Research, vol. 3, pp. 586–595, 2011. View at Google Scholar
  18. C. Chen, S. Duan, T. Cai, and B. Liu, “Online 24-h solar power forecasting based on weather type classification using artificial neural network,” Solar Energy, vol. 85, no. 11, pp. 2856–2870, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. F. O. Hocaoǧlu, Ö. N. Gerek, and M. Kurban, “Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks,” Solar Energy, vol. 82, no. 8, pp. 714–726, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Linares-Rodríguez, J. A. Ruiz-Arias, D. Pozo-Vázquez, and J. Tovar-Pescador, “Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks,” Energy, vol. 36, no. 8, pp. 5356–5365, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Paoli, C. Voyant, M. Muselli, and M.-L. Nivet, “Forecasting of preprocessed daily solar radiation time series using neural networks,” Solar Energy, vol. 84, no. 12, pp. 2146–2160, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Chen and S. Varadarajan, “Integration of design of experiments and artificial neural networks for achieving affordable concurrent design,” in Proceedings of the 38th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference and AIAA/ASME/AHS Adaptive Structures Forum, pp. 1316–1324, April 1997. View at Scopus
  23. A. Mellit and S. Shaari, “Recurrent neural network-based forecasting of the daily electricity generation of a Photovoltaic power system,” in Ecological Vehicle and Renewable Energy (EVER), pp. 26–29, Monte-Carlo, Monaco, March 2009.
  24. E. A. Ahmed and M. E.-N. Adam, “Estimate of global solar radiation by using Artificial Neural Network in Qena, Upper Egypt,” Journal of Clean Energy Technologies, vol. 1, no. 2, pp. 148–150, 2013. View at Publisher · View at Google Scholar
  25. S. Naini, “Evaluation of RBF, GR and FFBP neural networks for prediction of geometrical dimensions of scour hole below ski-jump spillway,” in Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems (ICECS '11), vol. 19, pp. 89–93, IACSIT Press, Singapore, 2011.
  26. T. Vigneswaran and S. Dhivya, “Analyzing the probabilistic distribution of the predicted wind speed,” International Journal of Computer and Information Technology, vol. 1, pp. 88–93, 2012. View at Google Scholar
  27. K. Upadhyay, A. Choudhary, and M. Tripathi, “Short-term wind speed forecasting using feed-forward back-propagation neural network,” International Journal of Engineering, Science and Technology, vol. 3, no. 5, pp. 107–112, 2011. View at Google Scholar
  28. F. A. Makinde, C. T. Ako, O. D. Orodu, and I. U. Asuquo, “Prediction of crude oil viscosity using feed-forward back-propagation neural network (FFBPNN),” Petroleum and Coal, vol. 54, pp. 120–131, 2012. View at Google Scholar
  29. C. J. Devi, B. S. P. Reddy, K. V. Kumar, B. M. Reddy, and N. Raja, “ANN approach for weather prediction using back propagation,” International Journal of Engineering Trends and Technology, vol. 3, no. 1, 2012. View at Google Scholar
  30. K. O. Alawode and M. O. Oyedeji, “A comparison of neural network models for load forecasting in nigerian power system,” International Journal of Research in Engineering and Technology, vol. 2, no. 5, 2013. View at Google Scholar
  31. T. Krishnaiah, S. Srinivasa Rao, K. Madhumurthy, and K. Reddy, “Neural network approach for modelling global solar radiation,” Journal of Applied Sciences Research, vol. 3, pp. 1105–1111, 2007. View at Google Scholar
  32. S. Zhao, J. Zhao, G. Zhao, W. Zhang, and Z. Guo, “Effective wind power density prediction based on neural networks,” in Proceedings of the International Conference on Multimedia Technology (ICMT '10), pp. 1–4, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. P. M. Gotovtsev, D. S. Smetanin, and V. N. Voronov, “Prediction of water chemistry state by means of artificial neural network,” in International Conference on the Properties of Water and Steam, pp. 1–3, September 2008.
  34. G. Corani, “Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning,” Ecological Modelling, vol. 185, no. 2–4, pp. 513–529, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Khatib, A. Mohamed, K. Sopian, and M. Mahmoud, “Solar energy prediction for Malaysia using artificial neural networks,” International Journal of Photoenergy, vol. 2012, Article ID 419504, 16 pages, 2012. View at Publisher · View at Google Scholar
  36. D. F. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568–576, 1991. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Budischak, D. Sewell, H. Thomson, L. Mach, D. E. Veron, and W. Kempton, “Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time,” Journal of Power Sources, vol. 225, pp. 60–74, 2013. View at Google Scholar
  38. Sharp data sheet, 2007, http://www.sharpcentrum.com/fileadmin/dokumenty/solar_panely/Sharp_NU-S0E3E_NU-180_E1__180Wp_NU-S5E3E_NU-185_E1__185Wp_EN.pdf.
  39. T. Jayalakshmi and A. Santhakumaran, “Statistical normalization and back propagation for classification,” International Journal of Computer Theory and Engineering, vol. 3, pp. 1793–8201, 2011. View at Google Scholar
  40. I. Popescu, P. Constantinou, M. Nafornita, and L. Nafornita, “Generalized Regression Neural Network prediction model for indoor environment,” in Proceedings of the 9th International Symposium on Computers and Communications (ISCC '04), pp. 657–661, July 2004. View at Scopus