Paper Info
Title
Bayesian predictive models for Rayleigh wind speed
Abstract
One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view the distributions of the wind speed over a wind farm as being homogeneous. However, the uncertainty about these wind speed models has not yet been considered. In this paper the Bayesian approach to taking into account the uncertainty inherent in the wind speed model has been presented. The proposed Bayesian predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines' locations in a wind farm. More specifically, instead of using a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented.
Year
DOI
Venue
2017
10.1109/ICUWB.2017.8251009
2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB)
Keywords
Field
DocType
Prior distribution,Posterior distribution,Markov Chain Monte Carlo (MCMC),Gamma prior (key words)
Statistical physics,Rayleigh scattering,Data modeling,Wind speed,Posterior probability,Probability distribution,Prior probability,Scale parameter,Mathematics,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-1-5090-5008-6
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Amir Shahirinia100.34
Amin Hajizadeh286.62
D. Yu310.79