Over the past few years a number of new mathematical functions have been proposed for wind speed probability density distributions. The most commonly used function that has been cited in literature has been the two-parameter Weibull function. However, in recent years studies have shown that the two-parameter Weibull function might be inadequate in modeling the wind speed probability density distributions or independent of whether the distribution is of unimodal or bimodal nature. For the unimodal distributions, the inadequacy may be due to the intricate behavior of the distribution, which prevents it to be satisfyingly modeled by a two-parameter model. For the bimodal behavior, the two-parameter Weibull function, which produces only a unimodal distribution, is simply inadequate to model it appropriately. Therefore, in recent years, alternative functions have been suggested for both unimodal and bimodal distributions, seeking more involved functions to better model these distributions. This article involves the modeling of observed wind speed probability density distributions using the main body of models found in the literature, namely, Rayleigh, Lognormal, two-parameter Weibull, three-parameter Weibull, and bimodal Weibull probability distribution functions. One of the important steps in the evaluation of different functions is the interpretation of the statistical parameters, namely, slope, R2, mean bias error, and root mean squared error, as are presently used in this article. A novel statistical tool is developed in the present article using these four statistical parameters. The novel tool can be used to evaluate the relative performance of models when more than one model is involved or to determine the overall accuracy of a particular model for a specific site. The calculations are made based on the long term wind speed data collected at 4-s interval at the experimental site at Edinburgh Napier University.Over the past few years a number of new mathematical functions have been proposed for wind speed probability density distributions. The most commonly used function that has been cited in literature has been the two-parameter Weibull function. However, in recent years studies have shown that the two-parameter Weibull function might be inadequate in modeling the wind speed probability density distributions or independent of whether the distribution is of unimodal or bimodal nature. For the unimodal distributions, the inadequacy may be due to the intricate behavior of the distribution, which prevents it to be satisfyingly modeled by a two-parameter model. For the bimodal behavior, the two-parameter Weibull function, which produces only a unimodal distribution, is simply inadequate to model it appropriately. Therefore, in recent years, alternative functions have been suggested for both unimodal and bimodal distributions, seeking more involved functions to better model these distributions. This article involve...
[1]
Chandrabhan Sharma,et al.
Wind Speed Distributions: A New Catalogue of Defined Models
,
2001
.
[2]
R. Corotis,et al.
Probability models of wind velocity magnitude and persistence
,
1978
.
[3]
A. Brett,et al.
The Characteristics of Wind Velocity that Favor the Fitting of a Weibull Distribution in Wind Speed Analysis
,
1984
.
[4]
Ervin Bossanyi,et al.
Wind Energy Handbook
,
2001
.
[5]
A. Dorvlo.
Estimating wind speed distribution
,
2002
.
[6]
Qiusheng Li,et al.
Probability distributions of extreme wind speed and its occurrence interval
,
2005
.
[7]
A. Sayigh,et al.
Wind characteristics and wind energy potential in Morocco
,
1998
.
[8]
Tariq Muneer,et al.
Discourses on solar radiation modeling
,
2007
.
[9]
T. W. Lambert,et al.
Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis
,
2000
.
[10]
O. A. Jaramillo,et al.
Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case
,
2004
.
[11]
A. Celik.
A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey
,
2004
.
[12]
F. L. Ludwig,et al.
A practical and economic method for estimating wind characteristics at potential wind energy conversion sites
,
1980
.
[13]
Gareth Harrison,et al.
Energy and carbon audit of a rooftop wind turbine
,
2006
.
[14]
J. A. Carta,et al.
A continuous bivariate model for wind power density and wind turbine energy output estimations
,
2007
.
[15]
Ali Naci Celik,et al.
Energy output estimation for small-scale wind power generators using Weibull-representative wind data
,
2003
.
[16]
Stelios Pashardes,et al.
Statistical analysis of wind speed and direction in Cyprus
,
1995
.
[17]
V. Morgan.
Statistical distributions of wind parameters at Sydney, Australia
,
1995
.
[18]
K. P. Pandey,et al.
Analysis of wind regimes for energy estimation
,
2002
.
[19]
J. A. Carta,et al.
Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago
,
2007
.
[20]
Željko Tomšić,et al.
Feasibility analysis of wind-energy utilization in Croatia
,
1999
.
[21]
J. Torres,et al.
Fitting wind speed distributions: a case study
,
1998
.
[22]
J. A. Carta,et al.
Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions
,
2007
.
[23]
R. Pallabazzer,et al.
Wind resources of Somalia
,
1991
.
[24]
E. Takle,et al.
Note on the Use of Weibull Statistics to Characterize Wind-Speed Data
,
1978
.
[25]
D. Lalas,et al.
An analysis of wind power potential in Greece
,
1983
.
[26]
J. A. Carta,et al.
Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study
,
2005
.