Estimating wind speed probability distribution using kernel density method

Abstract Accurate estimation of long term wind speed probability distribution is a fundamental and challenging task in wind energy planning. This paper proposes a nonparametric kernel density estimation method for wind speed probability distribution. The proposed method is compared with ten conventional parametric distribution models for wind speed that have been presented in literatures so far. The results demonstrate that the proposed non-parametric estimation is more accurate and has better adaptability than any conventional parametric distribution for wind speed.

[1]  Chandrabhan Sharma,et al.  Wind Speed Distributions: A New Catalogue of Defined Models , 2001 .

[2]  K. Conradsen,et al.  Review of Weibull Statistics for Estimation of Wind Speed Distributions , 1984 .

[3]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[4]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[5]  Uday Kumar,et al.  Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran , 2008, Reliab. Eng. Syst. Saf..

[6]  Mikhail Alexandrov,et al.  A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function , 2000, Appl. Math. Comput..

[7]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[8]  Vladimir Katkovnik,et al.  A new method for varying adaptive bandwidth selection , 1999, IEEE Trans. Signal Process..

[9]  Chao He,et al.  Probability Density Estimation from Optimally Condensed Data Samples , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  W. E. Bardsley.  Note on the Use of the Inverse Gaussian Distribution for Wind Energy Applications , 1980 .

[11]  J. C. Lam,et al.  A study of Weibull parameters using long-term wind observations , 2000 .

[12]  M. Rosenblatt.  Remarks on Some Nonparametric Estimates of a Density Function , 1956 .

[13]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[14]  David Suter,et al.  Robust adaptive-scale parametric model estimation for computer vision , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Valerio Lo Brano,et al.  Quality of wind speed fitting distributions for the urban area of Palermo, Italy , 2011 .

[16]  L. V. Auwera,et al.  The Use of the Weibull Three-Parameter Model for Estimating Mean Wind Power Densities , 1980 .

[17]  H. W. Church,et al.  ESTIMATION OF LONG TERM CONCENTRATIONS USING A UNIVERSAL WIND SPEED DISTRIBUTION , 1974 .

[18]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[19]  A. Celik.  On the distributional parameters used in assessment of the suitability of wind speed probability density functions , 2004 .

[20]  A. Ullah,et al.  Nonparametric Econometrics , 1999 .