Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/50523
Título: Statistical modelling of directional wind speeds using mixtures of von Mises distributions: Case study
Autores/as: Carta, José A. 
Bueno, Celia
Ramírez, Penélope
Palabras clave: Energy
Fecha de publicación: 2008
Editor/a: 0196-8904
Publicación seriada: Energy Conversion and Management 
Resumen: A finite mixture model of continuous variable probability is used in this paper to represent the distribution of directional wind speed. The model is comprised of a finite mixture of von Mises (vM-pdf) distributions.The parameters of the model are estimated using the least squares method. The range of integration to compute the mean angle and the standard deviation of wind direction is adjusted to minimum variance requirements. The suitability of the distributions is judged from the coefficient of determination R-2. The model is applied in this paper to wind direction data recorded at several weather stations located in the Canary Islands (Spain). The conclusion reached is that the mixture distribution used in this paper provides a very flexible model for wind direction studies and can be applied in a widespread manner to represent the wind direction regimes in zones with several modes or prevailing wind directions. In the case of the Canary Islands, mixtures of two vM-pdfs provide better fits in all the cases analysed than those obtained with the models proposed in the specialised literature oil wind energy. The conclusion is also drawn that when the number of components N of the mixture distribution increases, tile value of R-2 increases. However, the variations in R-2 are not significant for values of N greater than six. (C) 2007 Elsevier Ltd. All rights reserved.
URI: http://hdl.handle.net/10553/50523
ISSN: 0196-8904
DOI: 10.1016/j.enconman.2007.10.017
Fuente: Energy Conversion and Management[ISSN 0196-8904],v. 49, p. 897-907
Colección:Artículos
Vista completa

Citas SCOPUSTM   

132
actualizado el 17-nov-2024

Citas de WEB OF SCIENCETM
Citations

107
actualizado el 17-nov-2024

Visitas

33
actualizado el 02-dic-2023

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.