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Journal of Renewable Energy
Volume 2014 (2014), Article ID 986830, 15 pages
Research Article

Comparison and Optimization of Neural Networks and Network Ensembles for Gap Filling of Wind Energy Data

1Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA
2Department of Geography, Ruhr University Bochum, 44780 Bochum, Germany

Received 28 January 2014; Revised 15 April 2014; Accepted 16 April 2014; Published 26 May 2014

Academic Editor: Shuhui Li

Copyright © 2014 Andres Schmidt and Maya Suchaneck. 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. IPCC, “Climate Change 2013: The Physical Science Basis,” in Contribution of Working Group I To the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. Qin, G. K. Plattner et al., Eds., p. 1535, Cambridge University Press, Cambridge, United Kingdom, 2013. View at Google Scholar
  2. P. Friedlingstein, R. A. Houghton, G. Marland et al., “Update on CO2 emissions,” Nature Geoscience, vol. 3, no. 12, pp. 811–812, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. G. P. Peters, G. Marland, C. le Quéré, T. Boden, J. G. Canadell, and M. R. Raupach, “Rapid growth in CO2 emissions after the 2008-2009 global financial crisis,” Nature Climate Change, vol. 2, no. 1, pp. 2–4, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. G. P. Peters, C. L. Weber, D. Guan, and K. Hubacek, “China's growing CO2 emissions - A race between increasing consumption and efficiency gains,” Environmental Science and Technology, vol. 41, no. 17, pp. 5939–5944, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Lu, M. B. McElroy, and J. Kiviluoma, “Global potential for wind-generated electricity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 27, pp. 10933–10938, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. EEG, “Renewable energy sources Act of 25 October 2008,” last amended by the Act of 20 December 2012, Art. 5G I, 2730, 2012.
  7. I. Troen and E. L. Petersen, European Wind Atlas, Risø National Laboratory, Roskilde, Denmark, 1989.
  8. N. G. Mortensen, L. Landberg, I. Troen, and E. L. Petersen, “Wind Atlas Analysis and Application Program (WAsP), Vol. 1: Getting Started. Vol. 2: User’s Guide,” Risø National Laboratory, Roskilde, Denmark, 1993.
  9. R. Gasdch and J. Twele, Wind Power Plants: Fundamentals, Design, Construction and Operation, Springer, Berlin, Germany, 2nd edition, 2012.
  10. W. Winkler, M. Strack, and A. Westerhellenweg, “Scaling and evaluation of wind data and wind farm energy yields,” DEWI Magazine, vol. 23, pp. 76–84, 2003. View at Google Scholar
  11. FGW, “Technical Guidelines for Wind Turbines. Part 6. Determination of wind potential and energy yields,” Revision 8, Berlin, Germany, 2011.
  12. C. M. Bishop, Neural Networks For Pattern Recognition, Oxford University Press, Oxford, UK, 1996.
  13. D. W. Patterson, Artificial Neural Networks. Theory and Applications, Prentice Hall, Singapore, 1996.
  14. D. S. Broomhead and D. Lowe, “Multi variable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355, 1988. View at Google Scholar
  15. B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, UK, 1996.
  16. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, USA, 2nd edition, 1999.
  17. S. Li, D. C. Wunsch, E. A. O'Hair, and M. G. Giesselmann, “Using neural networks to estimate wind turbine power generation,” IEEE Transactions on Energy Conversion, vol. 16, no. 3, pp. 276–282, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Liu, W. Gao, Y. H. Wan, and E. Muljadi, “Wind power plant prediction by using neural networks,” in Proceedings of the IEEE Energy Conversion Conference and Exposition, Raleigh, NC, USA, September 2012.
  19. W. A. Oost and E. M. Oost, “An alternative approach to the parameterization the momentum flux over the sea,” Boundary-Layer Meteorology, vol. 113, no. 3, pp. 411–426, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Ayoubi and K. L. Sahrawat, “Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in Northern Iran,” Archives of Agronomy and Soil Science, vol. 57, no. 5, pp. 549–565, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Z. Hamid Zare Abyaneh, “Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters,” Journal of Environ Health Science and Engineering, vol. 12, article 40, 2014. View at Publisher · View at Google Scholar
  22. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McClelland, and PDP Research Group, Eds., vol. 1, pp. 318–362, Cambridge University Press, Cambridge, Mass, USA, 1986. View at Google Scholar
  23. E. Metcalfe, J. Teteh, and S. L. Howells, “Optimisation of radial basis and backpropagation neural networks for modelling auto-ignition temperature by quantitative-structure property relationships,” in Chemometrics and Intelligent Laboratory Systems, vol. 32, pp. 177–191, 1996. View at Google Scholar
  24. M. Hestenes, Conjugate Direction Methods in Optimization, Springer, New York, NY, USA, 1980.
  25. G. J. Bowden, H. R. Maier, and G. C. Dandy, “Optimal division of data for neural network models in water resources applications,” Water Resources Research, vol. 38, no. 2, pp. 2–11, 2002. View at Google Scholar · View at Scopus
  26. R. Khosla, “Knowledge-Based Intelligent Information and Engineering Systems,” in Proceedings of the KES 9th International Conference, Lecture Notes in Computer Science, p. 933, Melbourne, Australia, September 2005.
  27. A. Schmidt, C. Hanson, J. Kathilankal, and B. E. Law, “Classification and assessment of turbulent fluxes above ecosystems in North-America with self-organizing feature map networks,” Agricultural and Forest Meteorology, vol. 151, no. 4, pp. 508–520, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Smith and H. Link, “Anemometer measurements for use in turbine power performance tests,” Preprint American Wind Energy Association (AWEA) Windpower Conference Portland, Oregon, USA, June 2002.
  29. A. Albers and H. Sacman, “Wind speed and turbulence evaluation for performance and gondola anemometer measurements,” DEWI Magazine, vol. 10, pp. 51–62, 1997. View at Google Scholar
  30. J. A. Anderson, An Introduction To Neural Networks, MIT Press, Boston, Mass, USA, 1995.
  31. M. Qu, F. Y. Shih, J. Jing, and H. Wang, “Automatic solar flare detection using MLP, RBF, and SVM,” Solar Physics, vol. 217, no. 1, pp. 157–172, 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Schmidt, T. Wrzesinsky, and O. Klemm, “Gap filling and quality assessment of CO2 and water vapour fluxes above an urban area with radial basis function neural networks,” Boundary-Layer Meteorology, vol. 126, no. 3, pp. 389–413, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. N. Maharani, S. Lee, and Y. K. Lee, “Topographical effects on wind speeds over various terrains: a case study for Korean peninsula,” in Proceedings of the 7th Asia-Pacific Conference on Wind Engineering, Taipei, Taiwan, November 2009.
  34. M. P. Perrone and L. N. Cooper, “When networks disagree: ensemble methods for hybrid neural networks,” in Artificial Neural Networks For Speech and Vision, R. J. Mammone, Ed., pp. 126–147, Chapman & Hall/CRC, London, UK.
  35. B. Bakker and T. Heskes, “Clustering ensembles of neural network models,” Neural Networks, vol. 16, no. 2, pp. 261–269, 2003. View at Publisher · View at Google Scholar · View at Scopus
  36. K. J. Eidsvik, “A system for wind power estimation in mountainous terrain. prediction of askervein hill data,” Wind Energy, vol. 8, no. 2, pp. 237–249, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. J. M. Zurada, A. Malinowski, and I. Cloete, “Sensitivity analysis for minimization of input data dimension for feedforward neural networks,” in Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 447–450, IEEE Press, London, UK, February 1994.
  38. R. E. Bellman, Adaptive Control Processes, Princeton University Press, Princeton, NJ, USA, 1961.