Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Characterization and Estimation of Wind Energy Resources Using Autoregressive Modelling and Probability Density Functions

Characterization and Estimation of Wind Energy Resources Using Autoregressive Modelling and... The commonly used two-parameter Weibull distribution has fitted many wind speed distributions reasonably well. However, the use of such probability density functions (PDFs) does not provide a very accurate description of wind resources. The main drawback is that the wind time series data are assumed to be uncorrelated over time. This paper studies autoregressive (AR) modelling as an alternative method to characterize a given wind regime. The AR model allows for a very accurate representation of the wind resource and additionally, provides a tool for its forecasting. The AR model is compared with commonly used non-Gaussian distributions for two Caribbean wind-speed time-series. The comparison evaluates the fitting of the models, and, most importantly, the estimated values of power and energy to be extracted from the wind resource. It is concluded that autoregressive (AR) modelling provides greater accuracy than the popular Weibull and Rayleigh distributions, especially for low wind-speed systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wind Engineering SAGE

Characterization and Estimation of Wind Energy Resources Using Autoregressive Modelling and Probability Density Functions

Castellanos, F.; Ramesar, Vincent Isa
Wind Engineering , Volume 30 (1): 14 – Jan 1, 2006
Download PDF
14 pages

Loading next page...
 
/lp/sage/characterization-and-estimation-of-wind-energy-resources-using-JR3wcmqk1H

References (21)

Publisher
SAGE
Copyright
© 2006 SAGE Publications
ISSN
0309-524X
eISSN
2048-402X
DOI
10.1260/030952406777641397
Publisher site
See Article on Publisher Site

Abstract

The commonly used two-parameter Weibull distribution has fitted many wind speed distributions reasonably well. However, the use of such probability density functions (PDFs) does not provide a very accurate description of wind resources. The main drawback is that the wind time series data are assumed to be uncorrelated over time. This paper studies autoregressive (AR) modelling as an alternative method to characterize a given wind regime. The AR model allows for a very accurate representation of the wind resource and additionally, provides a tool for its forecasting. The AR model is compared with commonly used non-Gaussian distributions for two Caribbean wind-speed time-series. The comparison evaluates the fitting of the models, and, most importantly, the estimated values of power and energy to be extracted from the wind resource. It is concluded that autoregressive (AR) modelling provides greater accuracy than the popular Weibull and Rayleigh distributions, especially for low wind-speed systems.

Journal

Wind Engineering – SAGE

Published: Jan 1, 2006

There are no references for this article.