SciELO - Scientific Electronic Library Online

vol.23 issue2A simple demand-side management solution for a typical compressed-air system at a South African gold mineExperimental analysis of a solar absorption system with interior energy storage author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



Related links

  • On index processCited by Google
  • On index processSimilars in Google


Journal of Energy in Southern Africa

On-line version ISSN 2413-3051
Print version ISSN 1021-447X

J. energy South. Afr. vol.23 n.2 Cape Town  2012


Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters



Temitope R Ayodele; Adisa A Jimoh; Josiah L Munda; John T Agee

Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa




This paper analyses wind speed characteristics and wind power potential of Port Elizabeth using statistical Weibull parameters. A measured 5-minute time series average wind speed over a period of 5 years (2005 - 2009) was obtained from the South African Weather Service (SAWS). The results show that the shape parameter (k) ranges from 1.319 in April 2006 to 2.107 in November 2009, while the scale parameter (c) varies from 3.983m/s in May 2008 to 7.390 in November 2009.The average wind power density is highest during Spring (September-October), 256.505W/m2 and lowest during Autumn (April-May), 152.381W/m2. This paper is relevant to a decision-making process on significant investment in a wind power project.

Keywords: statistical analysis, wind power density, wind speed, Weibull parameters, Port Elizabeth



Full text available only in PDF format.



The authors want to thank the Tshwane University of Technology for the support of this research and also the South African Weather Services for providing the data used for the study.


References, S. (2010). South Africa Energy supply. (Accessed 10 June 2010)        [ Links ]

Akpinar, E. K. & Akpinar S. (2005). A Statistical Analysis of Wind Speed Data Used in Installation of Wind Energy Conversion Systems. Energy Conversion and Management, 46, 515-532.         [ Links ]

Bilgili, M., Sahin, B. & Kahraman, A. (2004). Wind Energy Potential in Antakya and Iskenderun region, Turkey. Renewable Energy, 29, 1733-1745.         [ Links ]

Celik, A. N. (2004). A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy, 29, 593-604.         [ Links ]

Darling Wind Farm Project Fact Sheet Darling Wind Power (pty) Ltd, (Accessed 15 July 2010).         [ Links ]

Di Piazza, A., Di Piazza, M. C., Ragusa, A. & Vitale, G. (2010). Statistical Processing of Wind Speed Data for Energy Forecast and Planning. International Conference on Renewable Energies and Power Quality (ICRPQ,10). Granada, Spain.         [ Links ]

Gupta, R. & Biswas, A. (2010). Wind Data Analysis of Silchar (Assam, India) by Rayleigh,s and Weibull Methods. Journal of Mechanical Engineering Research, 2, 10-24.         [ Links ]

Harter, H. L. & Moore, A. H. (1965). Maximum-Likelihood Estimation of the Parameters of Gamma and Weibull Populations from Complete and from Censored Sample. American Society for Quality (Technometrics), 7, 639-643.         [ Links ]

Lu, L., Yang, H. & Burnett, J. (2002). Investigation on Wind Power Potential on Hong Kong Islands-an Analysis of Wind Power and Wind Turbine Characteristic. Renewable Energy, 27, 1-12.         [ Links ]

Lun, I. Y. F. & Lam, J. C. (2000). A Study of Wibull Parameters Using Long-term Wind Observations. Renewable Energy, 20, 145-153.         [ Links ]

Nigim, K. A. & Parker, P. (2007). Heuristic and Probabilistic Wind power Availability Estimation Procedures: Improved Tools For Technology and Site Selection. Renewable Energy, 32, 638-648. Refocus. South Africa's Transition Towards Renewable Energy Clear Intentions? Pretoria, (Accessed 15 July 2010).         [ Links ]

SAWEA. IRP 2010. Draft Parameter comments. South African Wind Energy Association (Accessed 15 July 2010 )        [ Links ]

Seguro, J. V. & Lambert, T. W. (2000). Modern Estimation of the Parameters of Weibull Wind Speed Distribution for Wind Energy Analysis. Journal of Wind Engineering and Industrial Aerodynamics, 85, 75-84.         [ Links ]

Ulgen, K. & Hepbasli, A. (2002). Determination of Weibull Parameters for Wind Energy Analysis of Izmir, Turkey. International Journal of Energy Research, 26, 495-506.         [ Links ]

Weisser, D. (2003). A Wind Energy Analysis of Grenada: An Estimation Using The Weibull Density Function. Renewable Energy, 28, 1803-1812.         [ Links ]

Zaharim, A., Razali, A. M., Abidin, R. Z. & Sopian, K. (2009). Fitting of Statistical Distributions to Wind Speed Data in Malaysia. European Journal of Scientific Research, 26, 6-12.         [ Links ]



Received 30 November 2010
Revised 24 February 2012

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License