@inproceedings{b348125f25fc41b2b91ed2d7c81ff435,
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{\textquoteright} 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.",
keywords = "Prior distribution, Posterior distribution, Markov Chain Monte Carlo (MCMC), Gamma prior",
author = "Amir Shahirinia and Amin Hajizadeh and Yu, {David C}",
year = "2017",
month = sep,
doi = "10.1109/ICUWB.2017.8251009",
language = "English",
series = "IEEE International Conference on Ultra-Wideband (ICUWB)",
booktitle = "Proceedings of 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB)",
publisher = "IEEE Press",
note = "2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB) ; Conference date: 12-09-2017 Through 15-09-2017",
}