Preprints
https://doi.org/10.5194/wes-2022-70
https://doi.org/10.5194/wes-2022-70
 
23 Aug 2022
23 Aug 2022
Status: a revised version of this preprint is currently under review for the journal WES.

Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

M. Golam Mustafa Khan and M. Rafiuddin Ahmed M. Golam Mustafa Khan and M. Rafiuddin Ahmed
  • School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Laucala Campus, Suva, Republic of Fiji

Abstract. The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to assess energy at every location, especially at sites where low wind speeds are frequent, such as the Equatorial region. In this work, a robust technique for wind resource assessment using a Bayesian approach for estimating Weibull parameters is first proposed. Secondly, the wind resource assessment techniques using a two-parameter Weibull distribution and a three-parameter Weibull distribution, which is a generalized form of two-parameter Weibull distribution, are compared. Simulation studies confirm that the Bayesian approach seems a more robust technique for accurate estimation of Weibull parameters. The research is conducted using data from seven sites in Equatorial region from 1° N of Equator to 21° South of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shift parameter is a better fit for wind data having a higher percentage of low wind speeds (0–1 m/s) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results with a two-parameter distribution that is a special case of three-parameter Weibull distribution with zero shift parameter. The results also demonstrate that the proposed Bayesian approach and application of a three-parameter Weibull distribution are extremely useful for accurate estimate of wind power and annual energy production.

M. Golam Mustafa Khan and M. Rafiuddin Ahmed

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-70', Anonymous Referee #1, 22 Sep 2022
  • RC2: 'Comment on wes-2022-70', Anonymous Referee #2, 14 Oct 2022
  • AC1: 'Authors' Response to Comments on wes-2022-70', M. Rafiuddin Ahmed, 11 Nov 2022

M. Golam Mustafa Khan and M. Rafiuddin Ahmed

Data sets

Data Sets for three locations M.G.M. Khan and M. Rafiuddin Ahmed https://doi.org/10.21203/rs.3.rs-504670/v4

Model code and software

Appendix 1 M.G.M. Khan https://doi.org/10.21203/rs.3.rs-504670/v4

M. Golam Mustafa Khan and M. Rafiuddin Ahmed

Viewed

Total article views: 240 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
164 66 10 240 2 1
  • HTML: 164
  • PDF: 66
  • XML: 10
  • Total: 240
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 23 Aug 2022)
Cumulative views and downloads (calculated since 23 Aug 2022)

Viewed (geographical distribution)

Total article views: 217 (including HTML, PDF, and XML) Thereof 217 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Dec 2022
Download
Short summary
A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the Equatorial region from 1° N to 19° S of Equator revealed that the three-parameter Weibull distribution with non-zero shift parameter is a better fit for wind data having a higher percentage of low winds. Wind data with higher wind speeds is a special case of three-parameter distribution. This approach gives accurate results.