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Applications of Bayesian methods in wind energy conversion systems

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  • Li, Gong
  • Shi, Jing

Abstract

The fast growth of wind power is in urgent need of more accurate, reliable, and adaptive modeling and data analysis methods for the characterization and prediction of wind resource and wind power, as well as reliability evaluation of wind energy conversion systems. Bayesian methods have shown unique advantages in statistical modeling and data analysis for the quantity of interest with uncertainty and variability. The adoption of Bayesian methods carries great potentials for various aspects in wind energy conversion systems such as improving the accuracy and reliability of wind resource estimation and short-term forecasts. This paper summarizes the basic theories of several Bayesian methods, and extensively reviews the literature addressing the applications of Bayesian methods in wind energy conversion systems. Based on the state-of-the-art review, the prospects of Bayesian methods in wind energy conversion systems are discussed on how to develop new applications and enhance the methods for existing applications. It is believed that Bayesian methods will be gaining more momentum in wind energy applications in the near future.

Suggested Citation

  • Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    • Handle: RePEc:eee:renene:v:43:y:2012:i:c:p:1-8
    DOI: 10.1016/j.renene.2011.12.006
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    1. Han C. & Carlin B. P., 2001. "Markov Chain Monte Carlo Methods for Computing Bayes Factors: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1122-1132, September.
    2. Ward, Eric J., 2008. "A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools," Ecological Modelling, Elsevier, vol. 211(1), pages 1-10.
    3. Stuart Coles & Luis Pericchi, 2003. "Anticipating catastrophes through extreme value modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 405-416, October.
    4. Li, Hui & Jenkins-Smith, Hank C. & Silva, Carol L. & Berrens, Robert P. & Herron, Kerry G., 2009. "Public support for reducing US reliance on fossil fuels: Investigating household willingness-to-pay for energy research and development," Ecological Economics, Elsevier, vol. 68(3), pages 731-742, January.
    5. Xiao, Ni & Zarnikau, Jay & Damien, Paul, 2007. "Testing functional forms in energy modeling: An application of the Bayesian approach to U.S. electricity demand," Energy Economics, Elsevier, vol. 29(2), pages 158-166, March.
    6. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    7. Wilson, Alyson G. & Huzurbazar, Aparna V., 2007. "Bayesian networks for multilevel system reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1413-1420.
    8. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
    9. Ajayi, Oluseyi O., 2009. "Assessment of utilization of wind energy resources in Nigeria," Energy Policy, Elsevier, vol. 37(2), pages 750-753, February.
    10. López, P. & Velo, R. & Maseda, F., 2008. "Effect of direction on wind speed estimation in complex terrain using neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2266-2272.
    11. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    12. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    13. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    14. D. Walshaw, 2000. "Modelling extreme wind speeds in regions prone to hurricanes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 51-62.
    15. Dobigeon, Nicolas & Tourneret, Jean-Yves, 2007. "Joint segmentation of wind speed and direction using a hierarchical model," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5603-5621, August.
    16. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    17. Ackermann, Thomas & Söder, Lennart, 2000. "Wind energy technology and current status: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 4(4), pages 315-374, December.
    18. Blonbou, Ruddy, 2011. "Very short-term wind power forecasting with neural networks and adaptive Bayesian learning," Renewable Energy, Elsevier, vol. 36(3), pages 1118-1124.
    19. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    20. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
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