Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China

Qinkai Han, Tianyang Wang and Fulei Chu

Renewable and Sustainable Energy Reviews, 2022, vol. 161, issue C

Abstract: The joint probability density function can quantitatively describe the statistical characteristics and correlation features between wind speed and shear; this forms the theoretical basis for assessing height-dependent wind energy. Here, a nonparametric copula-based joint probability model of wind speed-wind shear is developed to assess height-dependent wind energy in China. Utilizing the transformation method and optimal bandwidth algorithms, a nonparametric copula model for wind speed/wind shear correlation analysis is proposed. Joint probability density models of wind speed and wind shear are then constructed. Various copula and marginal density models (including single parametric, mixture parametric, and kernel density estimation models) are evaluated at the regional scale. The nonparametric copula model exhibits remarkable superiority and is therefore deemed to be more suitable for wind speed/wind shear correlation analysis and joint probability modeling. When assessing height-dependent wind energy, the average distributions of wind turbine power output and capacity factor are obtained across mainland China. Additionally, this model could accurately analyze variations in wind power density with respect to hub height. These results can effectively facilitate accurate micro-site selection, thereby economically benefiting wind farms.

Keywords: Wind speed and shear; Copula models; Nonparametric modeling; Joint probability density function; Height-dependent wind energy assessment; Kernel density estimation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032122002337
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:161:y:2022:i:c:s1364032122002337

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2022.112319

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().