Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 785215, 11 pages
http://dx.doi.org/10.1155/2015/785215
Research Article

Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China

Received 13 October 2014; Accepted 24 April 2015

Academic Editor: Mehdi Khashei

Copyright © 2015 Kaikai Pan 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. G. W. E. Council, “Global wind statistics 2013,” Global Wind Report, 2013. View at Google Scholar
  2. J. C. Smith, M. R. Milligan, E. A. DeMeo, and B. Parsons, “Utility wind integration and operating impact state of the art,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 900–908, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. P. D. Fu, L. Hui, and L. Y. Fei, “A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm,” Power System Technology, vol. 32, pp. 82–86, 2008. View at Google Scholar
  4. H. Liu, E. Erdem, and J. Shi, “Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed,” Applied Energy, vol. 88, no. 3, pp. 724–732, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. R. G. Kavasseri and K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models,” Renewable Energy, vol. 34, no. 5, pp. 1388–1393, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. Y.-Y. Hong, H.-L. Chang, and C.-S. Chiu, “Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs,” Energy, vol. 35, no. 9, pp. 3870–3876, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. P. S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, “Short-term wind power forecasting in Portugal by neural networks and wavelet transform,” Renewable Energy, vol. 36, no. 4, pp. 1245–1251, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Salcedo-Sanz, E. G. Ortiz-García, Á. M. Pérez-Bellido, A. Portilla-Figueras, and L. Prieto, “Short term wind speed prediction based on evolutionary support vector regression algorithms,” Expert Systems with Applications, vol. 38, no. 4, pp. 4052–4057, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J. W. Zeng and W. Qiao, “Short-term solar power prediction using a support vector machine,” Renewable Energy, vol. 52, pp. 118–127, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Jiang, B. Dong, L. Xie et al., “Adaptive Gaussian process for short-term wind speed forecasting,” in Proceedings of the 19th European Conference on Artificial Intelligence (ECAI '10), vol. 215 of Frontiers in Artificial Intelligence and Applications, pp. 661–666, IOS Press, Lisbon, Portugal, August 2010.
  11. S. Al-Yahyai, Y. Charabi, and A. Gastli, “Review of the use of numerical weather prediction (NWP) models for wind energy assessment,” Renewable & Sustainable Energy Reviews, vol. 14, no. 9, pp. 3192–3198, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. M. G. de Giorgi, A. Ficarella, and M. Tarantino, “Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods,” Energy, vol. 36, no. 7, pp. 3968–3978, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Salcedo-Sanz, Á. M. Pérez-Bellido, E. G. Ortiz-García, A. Portilla-Figueras, L. Prieto, and D. Paredes, “Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction,” Renewable Energy, vol. 34, no. 6, pp. 1451–1457, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Salcedo-Sanz, Á. M. Pérez-Bellido, E. G. Ortiz-García, A. Portilla-Figueras, L. Prieto, and F. Correoso, “Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks,” Neurocomputing, vol. 72, no. 4–6, pp. 1336–1341, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Juban, N. Siebert, and G. N. Kariniotakis, “Probabilistic short-term wind power forecasting for the optimal management of wind generation,” in Proceedings of the IEEE Lausanne Power Tech, pp. 683–688, IEEE, Lausanne, Switzerland, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. J. B. Bremnes, “Probabilistic wind power forecasts using local quantile regression,” Wind Energy, vol. 7, no. 1, pp. 47–54, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Liu, J. Shi, and X. Qu, “Empirical investigation on using wind speed volatility to estimate the operation probability and power output of wind turbines,” Energy Conversion and Management, vol. 67, pp. 8–17, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Kou, F. Gao, and X. Guan, “Sparse online warped Gaussian process for wind power probabilistic forecasting,” Applied Energy, vol. 108, pp. 410–428, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Zhang, Y. Sun, G. Li, L. Cheng, and J. Lin, “A solution of economic dispatch problem considering wind power uncertainty,” Automation of Electric Power Systems, vol. 35, no. 22, pp. 125–130, 2011. View at Google Scholar · View at Scopus
  20. A. Fabbri, T. G. S. Román, J. R. Abbad, and V. H. M. Quezada, “Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market,” IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1440–1446, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. Z.-S. Zhang, Y.-Z. Sun, D. W. Gao, J. Lin, and L. Cheng, “A versatile probability distribution model for wind power forecast errors and its application in economic dispatch,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3114–3125, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. B.-M. Hodge and M. Milligan, “Wind power forecasting error distributions over multiple timescales,” in Proceedings of the IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future, Detroit, Mich, USA, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Hosansky, Weather forecast accuracy gets boost with new computer model, University Corporation for Atmospheric Research (UCAR), 2006.
  24. G. Li and J. Shi, “Application of Bayesian model averaging in modeling long-term wind speed distributions,” Renewable Energy, vol. 35, no. 6, pp. 1192–1202, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. M. E. Tipping, “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research, vol. 1, no. 3, pp. 211–244, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. S. R. Yang and H. Y. Shen, “Research and application of machine learning algorithm based on Relevance Vector Machine,” Computing Technology and Automation, vol. 29, pp. 43–47, 2010. View at Google Scholar
  27. B. Demir and S. Ertürk, “Hyperspectral image classification using relevance vector machines,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 586–590, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. N. Chen, Z. Qian, I. T. Nabney, and X. Meng, “Wind power forecasts using gaussian processes and numerical weather prediction,” IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 656–665, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Yan, Y. Liu, S. Han, and M. Qiu, “Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine,” Renewable & Sustainable Energy Reviews, vol. 27, pp. 613–621, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.
  31. P. Pinson and G. Kariniotakis, “Conditional prediction intervals of wind power generation,” IEEE Transactions on Power Systems, vol. 25, no. 4, pp. 1845–1856, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Bludszuweit, J. A. Domínguez-Navarro, and A. Llombart, “Statistical analysis of wind power forecast error,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983–991, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Xu, Y. Qiao, and Z. Lu, “A comprehensive error evaluation method for short-term wind power prediction,” Automation of Electric Power Systems, vol. 35, no. 12, pp. 20–26, 2011. View at Google Scholar · View at Scopus
  34. P. Pinson, H. A. Nielsen, J. K. Møller, H. Madsen, and G. N. Kariniotakis, “Non-parametric probabilistic forecasts of wind power: required properties and evaluation,” Wind Energy, vol. 10, no. 6, pp. 497–516, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Zhang, J. X. Wang, and X. F. Wang, “Review on probabilistic forecasting of wind power generation,” Renewable & Sustainable Energy Reviews, vol. 32, pp. 255–270, 2014. View at Publisher · View at Google Scholar · View at Scopus