Probability distributions of wind speed in the UAE

Energy Conversion and Management

Volumn 93, Issue , 2015, Pages 414-434

  Ouarda, T B M J ^a,b   Charron, C ^a   Shin, J Y ^a   Marpu, P R ^a   Al Mandoos, A H ^c   Al Tamimi, M H ^c   Ghedira, H ^a   Al Hosary, T N ^c  

^a KHALIFA UNIVERSITY   (United Arab Emirates)

^b CENTRE EAU TERRE ENVIRONNEMENT   (Canada)

^c National Centre of Meteorology and Seismology ^*  (United Arab Emirates)

Author keywords

Coefficient of determination; Kappa distribution; Mixture distribution; Model selection criteria; Non parametric model; Probability density function; Wind speed distribution

Indexed keywords

MIXTURES; PROBABILITY; PROBABILITY DENSITY FUNCTION; SPEED; WEIBULL DISTRIBUTION; WIND; WIND EFFECTS; WIND POWER;

COEFFICIENT OF DETERMINATION; KAPPA DISTRIBUTION; MIXTURE DISTRIBUTIONS; MODEL SELECTION CRITERIA; NON-PARAMETRIC MODEL; WIND SPEED DISTRIBUTION;

PROBABILITY DISTRIBUTIONS;

EID: 84922278881     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2015.01.036     Document Type: Article

References (64)

1. T.L. Acker, S.K. Williams, E.P.N. Duque, G. Brummels, and J. Buechler Wind resource assessment in the state of Arizona: inventory, capacity factor, and cost Renew Energy 32 9 2007 1453 1466
2. A.S. Ahmed Shata, and R. Hanitsch Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt Renew Energy 31 8 2006 1183 1202
3. E.K. Akpinar, and S. Akpinar A statistical analysis of wind speed data used in installation of wind energy conversion systems Energy Convers Manage 46 4 2005 515 532
4. S. Akpinar, and E.K. Akpinar Wind energy analysis based on maximum entropy principle (MEP)-type distribution function Energy Convers Manage 48 4 2007 1140 1149
5. S. Akpinar, and E.K. Akpinar Estimation of wind energy potential using finite mixture distribution models Energy Convers Manage 50 4 2009 877 884
6. A.H. Algifri Wind energy potential in Aden-Yemen Renew Energy 13 2 1998 255 260
7. C.L. Archer, and M.Z. Jacobson Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements J Geophys Res: Atmosph 108 D9 2003 4289
8. F. Ashkar, and T.B.M.J. Ouarda On some methods of fitting the generalized Pareto distribution J Hydrol 177 1-2 1996 117 141
9. L. Auwera, F. Meyer, and L. Malet The use of the Weibull three-parameter model for estimating mean power densities J Appl Meteorol 19 1980 819 825
10. T.R. Ayodele, A.A. Jimoh, J.L. Munda, and J.T. Agee Wind distribution and capacity factor estimation for wind turbines in the coastal region of South Africa Energy Convers Manage 64 2012 614 625
11. B. Bobée The Log Pearson Type 3 distribution and its application in hydrology Water Resour Res 11 5 1975 681 689
12. Bobée B, Ashkar F. Sundry averages method (SAM) for estimating parameters of the Log-Pearson Type 3 distribution. Research Report No. 251, INRS-ETE. Quebec City (Canada); 1988.
13. J.A. Carta, and P. Ramírez Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago Energy Convers Manage 48 1 2007 281 291
14. J.A. Carta, P. Ramirez, and S. Velazquez Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation Energy Convers Manage 49 10 2008 2647 2655
15. J.A. Carta, P. Ramirez, and S. Velazquez A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands Renew Sustain Energy Rev 13 5 2009 933 955
16. A.N. Celik Energy output estimation for small-scale wind power generators using Weibull-representative wind data J Wind Eng Ind Aerodyn 91 5 2003 693 707
17. A.N. Celik On the distributional parameters used in assessment of the suitability of wind speed probability density functions Energy Convers Manage 45 11-12 2004 1735 1747
18. T.P. Chang Estimation of wind energy potential using different probability density functions Appl Energy 88 5 2011 1848 1856
19. F. Chellali, A. Khellaf, A. Belouchrani, and R. Khanniche A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria Renew Sustain Energy Rev 16 1 2012 379 385
20. K. Conradsen, L.B. Nielsen, and L.P. Prahm Review of Weibull statistics for estimation of wind speed distributions J Climate Appl Meteorol 23 8 1984 1173 1183
21. C. Cunnane Unbiased plotting positions - a review J Hydrol 37 3-4 1978 205 222
22. A.S.S. Dorvlo Estimating wind speed distribution Energy Convers Manage 43 17 2002 2311 2318
23. Fichaux N, Ranchin T. Evaluating the offshore wind potential. A combined approach using remote sensing and statistical methods. In: Proceedings IGARSS'03 - IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France. Vol. 4; 2003, p. 2703-5.
24. A. Garcia, J.L. Torres, E. Prieto, and A. De Francisco Fitting wind speed distributions: a case study Sol Energy 62 2 1998 139 144
25. M. Gökçek, A. Bayülken, and Ş. Bekdemir Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey Renew Energy 32 10 2007 1739 1752
26. Guidoum AC. kedd: Kernel estimator and bandwidth selection for density and its derivatives. R package version 1.0.1. 2014. < http://CRAN.R-project.org/package=kedd >.
27. J.P. Hennessey Some aspects of wind power statistics J Appl Meteorol 16 2 1977 119 128
28. J.R.M. Hosking, and J.R. Wallis Regional frequency analysis: an approach based on L-moments 1997 Cambridge University Press
29. Hosking JRM. Fortran routines for use with the method of L-moments, version 3.04. Research report 20525, IBM Research Division; 1996.
30. Y. Hundecha, A. St-Hilaire, T.B.M.J. Ouarda, S. El Adlouni, and P. Gachon A nonstationary extreme value analysis for the assessment of changes in extreme annual wind speed over the gulf of St. Lawrence, Canada J Appl Meteorol Climatol 47 11 2008 2745 2759
31. M. Irwanto, N. Gomesh, M.R. Mamat, and Y.M. Yusoff Assessment of wind power generation potential in Perlis, Malaysia Renew Sustain Energy Rev 38 2014 296 308
32. O.A. Jaramillo, R. Saldaña, and U. Miranda Wind power potential of Baja California Sur, México Renew Energy 29 13 2004 2087 2100
33. M.C. Jones, J.S. Marron, and S.J. Sheather A brief survey of bandwidth selection for density estimation J Am Statist Assoc 91 433 1996 401 407
34. C.G. Justus, W.R. Hargraves, A. Mikhail, and D. Graber Methods for estimating wind speed frequency distributions J Appl Meteorol 17 3 1978 350 353
35. C.G. Justus, W.R. Hargraves, and A. Yalcin Nationwide assessment of potential output from wind-powered generators J Appl Meteorol 15 7 1976 673 678
36. R. Kose, M.A. Ozgur, O. Erbas, and A. Tugcu The analysis of wind data and wind energy potential in Kutahya, Turkey Renew Sustain Energy Rev 8 3 2004 277 288
37. M. Li, and X. Li MEP-type distribution function: a better alternative to Weibull function for wind speed distributions Renew Energy 30 8 2005 1221 1240
38. V. Lo Brano, A. Orioli, G. Ciulla, and S. Culotta Quality of wind speed fitting distributions for the urban area of Palermo, Italy Renew Energy 36 3 2011 1026 1039
39. N. Masseran, A.M. Razali, and K. Ibrahim An analysis of wind power density derived from several wind speed density functions: the regional assessment on wind power in Malaysia Renew Sustain Energy Rev 16 8 2012 6476 6487
40. M. Mirhosseini, F. Sharifi, and A. Sedaghat Assessing the wind energy potential locations in province of Semnan in Iran Renew Sustain Energy Rev 15 1 2011 449 459
41. E.C. Morgan, M. Lackner, R.M. Vogel, and L.G. Baise Probability distributions for offshore wind speeds Energy Convers Manage 52 1 2011 15 26
42. H. Nfaoui, J. Buret, and A.A.M. Sayigh Wind characteristics and wind energy potential in Morocco Sol Energy 63 1 1998 51 60
43. Ordóñez G, Osma G, Vergara P, Rey J. Wind and solar energy potential assessment for development of renewables energies applications in Bucaramanga, Colombia. In: IOP conference series: materials science and engineering, vol. 59(1); 2014, p. 012004.
44. T.B.M.J. Ouarda, C. Charron, K. Niranjan Kumar, P.R. Marpu, H. Ghedira, and A. Molini Evolution of the rainfall regime in the United Arab Emirates J Hydrol 514 2014 258 270
45. S. Persaud, D. Flynn, and B. Fox Potential for wind generation on the Guyana coastlands Renew Energy 18 2 1999 175 189
46. D. Petković, S. Shamshirband, N.B. Anuar, H. Saboohi, A.W. Abdul Wahab, and M. Protić An appraisal of wind speed distribution prediction by soft computing methodologies: a comparative study Energy Convers Manage 84 2014 133 139
47. D. Poje, and B. Cividini Assessment of wind energy potential in croatia Sol Energy 41 6 1988 543 554
48. Z.L. Qin, W.Y. Li, and X.F. Xiong Estimating wind speed probability distribution using kernel density method Electric Power Syst Res 81 12 2011 2139 2146
49. X. Qin, J.-S. Zhang, and X.-D. Yan Two improved mixture weibull models for the analysis of wind speed data J Appl Meteorol Climatol 51 7 2012 1321 1332
50. P. Ramirez, and J.A. Carta The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study Energy Convers Manage 47 15-16 2006 2564 2577
51. A.Z. Şahin, and A. Aksakal Wind power energy potential at the northeastern region of Saudi Arabia Renew Energy 14 1-4 1998 435 440
52. J.V. Seguro, and T.W. Lambert Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis J Wind Eng Ind Aerodyn 85 1 2000 75 84
53. V. Singh, and Z. Deng Entropy-based parameter estimation for kappa distribution J Hydrol Eng 8 2 2003 81 92
54. T. Soukissian Use of multi-parameter distributions for offshore wind speed modeling: the Johnson SB distribution Appl Energy 111 2013 982 1000
55. D.A. Stewart, and O.M. Essenwanger Frequency distribution of wind speed near the surface J Appl Meteorol 17 11 1978 1633 1642
56. M.Y. Sulaiman, A.M. Akaak, M.A. Wahab, A. Zakaria, Z.A. Sulaiman, and J. Suradi Wind characteristics of Oman Energy 27 1 2002 35 46
57. E.S. Takle, and J.M. Brown Note on use of Weibull statistics to characterize wind speed data J Appl Meteorol 17 4 1978 556 559
58. S.E. Tuller, and A.C. Brett The goodness of fit of the Weilbull and Rayleigh distribution to the distributions of observed wind speeds in a topographically diverse area J Climatol 5 1985 74 94
59. K. Ulgen, and A. Hepbasli Determination of Weibull parameters for wind energy analysis of Izmir, Turkey Int J Energy Res 26 6 2002 495 506
60. I. Usta, and Y.M. Kantar Analysis of some flexible families of distributions for estimation of wind speed distributions Appl Energy 89 1 2012 355 367
61. Water Resources Council, Hydrology Committee, 1967. A uniform technique for determining flood flow frequencies. US Water Resour. Counc., Bull. No. 15, Washington, D.C.
62. H. Zhang, Y.-J. Yu, and Z.-Y. Liu Study on the maximum entropy principle applied to the annual wind speed probability distribution: a case study for observations of intertidal zone anemometer towers of Rudong in East China Sea Appl Energy 114 2014 931 938
63. J. Zhang, S. Chowdhury, A. Messac, and L. Castillo A multivariate and multimodal wind distribution model Renew Energy 51 2013 436 447
64. J.Y. Zhou, E. Erdem, G. Li, and J. Shi Comprehensive evaluation of wind speed distribution models: a case study for North Dakota sites Energy Convers Manage 51 7 2010 1449 1458