Table of Contents Author Guidelines Submit a Manuscript
Journal of Solar Energy
Volume 2015 (2015), Article ID 410684, 13 pages
http://dx.doi.org/10.1155/2015/410684
Research Article

Spatial Approach of Artificial Neural Network for Solar Radiation Forecasting: Modeling Issues

1School of Engineering, Indian Institute of Technology Mandi (IIT Mandi), Room No. 106, Mandi Campus, Mandi 175005, India
2Mechanical Engineering Department, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, India
3School of Computing and Electrical Engineering, Indian Institute of Technology Mandi (IIT Mandi), Mandi 175005, India

Received 25 September 2014; Revised 5 December 2014; Accepted 18 December 2014

Academic Editor: Jayasundera M. S. Bandara

Copyright © 2015 Yashwant Kashyap 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. R. Marquez and C. F. M. Coimbra, “Intra-hour DNI forecasting based on cloud tracking image analysis,” Solar Energy, vol. 91, pp. 327–336, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. M. J. Ahmad and G. N. Tiwari, “Solar radiation models-a review,” International Journal of Energy Research, vol. 35, no. 4, pp. 271–290, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. V. Badescu, “Correlations to estimate monthly mean daily solar global irradiation: application to Romania,” Energy, vol. 24, no. 10, pp. 883–893, 1999. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Bakirci, “Models of solar radiation with hours of bright sunshine: a review,” Renewable and Sustainable Energy Reviews, vol. 13, no. 9, pp. 2580–2588, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. 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 · View at Scopus
  6. D. Stathakis, “How many hidden layers and nodes?” International Journal of Remote Sensing, vol. 30, no. 8, pp. 2133–2147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Zhang, Z. Wang, and D. Liu, “Global asymptotic stability of recurrent neural networks with multiple time-varying delays,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 855–873, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Wang and X. Zou, “Convergence of discrete-time neural networks with delays,” International Journal of Qualitative Theory of Differential Equations and Applications, vol. 2, pp. 24–37, 2008. View at Google Scholar
  9. X.-G. Liu, R. R. Martin, M. Wu, and M.-L. Tang, “Global exponential stability of bidirectional associative memory neural networks with time delays,” IEEE Transactions on Neural Networks, vol. 19, no. 3, pp. 397–407, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Prechelt, “Early stopping—but when?” in Neural Networks: Tricks of the Trade, vol. 7700 of Lecture Notes in Computer Science, pp. 53–67, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  11. R. Setiono, “Feedforward neural network construction using cross validation,” Neural Computation, vol. 13, no. 12, pp. 2865–2877, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. I. Gonzalez-Carrasco, A. Garcia-Crespo, B. Ruiz-Mezcua, and J. L. Lopez-Cuadrado, “Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches,” Applied Intelligence, vol. 35, no. 1, pp. 89–109, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Fujita, “Statistical estimation of the number of hidden units for feedforward neural networks,” Neural Networks, vol. 11, no. 5, pp. 851–859, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. S. I. Tamura and M. Tateishi, “Capabilities of a four-layered feedforward neural network: four layers versus three,” IEEE Transactions on Neural Networks, vol. 8, no. 2, pp. 251–255, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Zhang, X. Ma, and Y. Yang, “Bounds on the number of hidden neurons in three-layer binary neural networks,” Neural Networks, vol. 16, no. 7, pp. 995–1002, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. J.-Y. Li, T. W. Chow, and Y.-L. Yu, “The estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network,” in Proceedings of the IEEE International Conference on Neural Networks, IEEE, 1995.
  17. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, “Introduction to variational methods for graphical models,” Machine Learning, vol. 37, no. 2, pp. 183–233, 1999. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Yao and Y. Liu, “A new evolutionary system for evolving artificial neural networks,” IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 694–713, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. J. M. Sopena, E. Romero, and R. Alquezar, “Neural networks with periodic and monotonic activation functions: a comparative study in classification problems,” in Proceedings of the 9th International Conference on Artificial Neural Networks (ICANN '99), Conference Publication no. 470, pp. 323–328, IET, September 1999. View at Scopus
  20. B. Karlik and A. V. Olgac, “Performance analysis of various activation functions in generalized MLP architectures of neural networks,” International Journal of Artificial Intelligence and Expert Systems, vol. 1, no. 4, pp. 111–122, 2011. View at Google Scholar
  21. H. Yonaba, F. Anctil, and V. Fortin, “Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting,” Journal of Hydrologic Engineering, vol. 15, no. 4, Article ID 003004QHE, pp. 275–283, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. R. H. Inman, H. T. C. Pedro, and C. F. M. Coimbra, “Solar forecasting methods for renewable energy integration,” Progress in Energy and Combustion Science, vol. 39, no. 6, pp. 535–576, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. Nrel, India Solar Resource Data: Hourly, 2013.
  24. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, 1994.
  25. F. D. Marques, L. D. F. R. De Souza, D. C. Rebolho, A. S. Caporali, E. M. Belo, and R. L. Ortolan, “Application of time-delay neural and recurrent neural networks for the identification of a hingeless helicopter blade flapping and torsion motions,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 27, no. 2, pp. 97–103, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. N. Saravanan, A. Duyar, T.-H. Guo, and W. C. Merrill, “Modeling space shuttle main engine using feed-forward neural networks,” Journal of Guidance, Control, and Dynamics, vol. 17, no. 4, pp. 641–648, 1994. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Karsoliya, “Approximating number of hidden layer neurons in multiple hidden layer BPNN Architecture,” International Journal of Engineering Trends and Technology, vol. 3, no. 6, pp. 714–717, 2012. View at Google Scholar
  28. T. Taskaya-Temizel and M. C. Casey, “A comparative study of autoregressive neural network hybrids,” Neural Networks, vol. 18, no. 5-6, pp. 781–789, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Caudill and C. Butler, Understanding Neural Networks: Computer Explorations: A Workbook in Two Volumes with Software for the Macintosh and PC Compatibles, MIT Press, Boston, Mass, USA, 1994.
  30. K. Mehrotra, C. K. Mohan, and S. Ranka, Elements of Artificial Neural Networks, MIT Press, Cambridge, Mass, USA, 1997.
  31. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing, Boston, Mass, USA, 1996.
  32. R. R. Trippi and E. Turban, Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance, McGraw-Hill, New York, NY, USA, 1992.
  33. H. T. C. Pedro and C. F. M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,” Solar Energy, vol. 86, no. 7, pp. 2017–2028, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. E. W. Saad, D. V. Prokhorov, and D. C. Wunsch II, “Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks,” IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1456–1470, 1998. View at Publisher · View at Google Scholar · View at Scopus