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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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