Probability models for low wind speeds zones

This paper focuses on the comparison of some probability distributions for low wind speeds regimes using some goodness-of-t criteria. Results obtained from the analysis indicated that while the 2-parameter Weibull distribution reported the best t in terms of its highest p-value of the Kolmogorov-Smirnov (K-S) statistic, the Akaike information criterion (AIC) reported the 4-parameter transformed beta distribution as the best model for the wind speed observations. Further, the 1-parameter Maxwell distribution presented the best Q-Q plot of all the fitted distributions. The results from the study also suggest that several other probability distributions can be used as an efficient alternative to the conventional Weibull distribution in fitting wind speeds data from low wind speeds zones.

Agbetuyi, A.F., Akinbulire, T.O., Abdul Kareem, A.O., and Awosope, C.O.A. (2012). Wind energy potential in Nigeria. International Electrical Engineering Journal (IEEJ), 3: 595-601.

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19: 716723.

Brady, T.F. (2009). A simulation solution of the integration of wind power into an electricity generating network. Proceedings of the 2009 winter conference, 1523-1529.

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.

Datta, D., and Datta, D. (2013). Comparison of Weibull distribution and exponentiated Weibull distribution based estimation of mean and variance of Wind data. International Journal of Energy, Information and Communications, 4: 1-12.

Gupta, R., and Biswa, A. (2010). Wind data analysis of Silchar (Assam, India) by Rayleigh and Weibull methods. Journal of Mechanical Engineering Research, 2: 10-24.

Jaramillo, O.A., and Borja, M.A. (2004). Wind speed analysis in La ventosa, Mexico: A Bi-model probability distribution case. Renewable Energy, 29: 1613-1630.

Johnson, N.L., Kotz, S., and Balakrishnan, N. (1995). Continuous univariate distributions (second edition, vol. 2). New York, John Wiley & sons, Inc.

Klugman, S.A., Panjer, H.H., and Wilmot, G.E. (2008). Loss Models, From Data to Decisions (Third Edition), Wiley.

Masseran, N., Razal, A.M. Ibrahim, K., Zaharim A., and Sopian, K. (2013). The probability distribution model of wind speed over East Malaysia. Research Journal of

Applied Sciences, Engineering and Technology, 6: 1774-1779.

Odo, F.C., Oah, S.U., and Ugwuoke, P.E. (2012). Weibull distribution-based model for prediction of wind potential in Enugu, Nigeria. Advances in Applied Science Research, 3:1202-1208.

Osatohanmwen, P., Oyegue, F.O., and Ogbonmwan, S.M. (2016). Statistical analysis of wind energy potential in Benin City using the 2-parameter Weibull distribution. International Journal for Renewable Energy and Environment, 2: 22-31.

Perrin O., Rootzen H., Taesler R. (2006). A discussion of statistical methods used to estimate extreme wind speeds. Theoretical and Applied Climatology, 85: 203-215.

Petersen, T.L., Troen, I., Frandsen, S., and Hedegaard, K. (1981). Wind Atlas for Denmark, RISO, Denmark.

R Development Core Team.R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria; 2009. http://www.R-project.org.

Safari, B. (2011). Modeling wind speed and wind power distribution in Rwanda. Renewable and Sustainable Energy, Revision, 15: 925-935.

Sambo, A.S. (2005). Renewable energy for rural development: The Nigerian perspective. ISESCO Science and Technology Vision, 1: 16-18.

Sarkar, A., and Kasperki, M. (2009). Weibull parameters for Wind Speed distribution in India. Proceedings of 5th National Conference on Wind Engineering, pp. 134-158.

Sarkar A., Singh, S. and Mitra, D. (2011). Wind Climate modeling using Weibull and Extreme value distribution. International Journal of Engineering Science and Technology, 3: 100-106.

Slootweg, J.C., Haan, S.W.H., Polinder, H., and Kling, W.L. (2001). Modeling wind turbines in power system dynamics simulation. Proceedings of the Power Engineering Society Summer Meeting Conference, 1: 22-26.

Ulgen, K., and Hepbasli, A. (2002). Determination of Weibull parameters for wind Energy analysis of Izmir, Turkey. International Journal of Energy Research, 26: 495-506.

Walck, C. (2007). Handbook on statistical distributions for experimentalists. Internal Report SUF-PFY/96-01.

Wentink, T. (1976). Study of Alaskan wind power potential and its possible application. Final Rep., Rep No. NSF/RANN/SE/AER 74-0023/FR 76/1, GeophysicalInstitute, University of Alaska.

Yilmaz, V., and Celik, H.E. (2008). A statistical approach to estimate the wind speed distribution: The Case of Gelibolu region. Dogus University, Dergisi, 9: 122-132.

Zaharim, A., Razali, A.M., Abidin, R.Z., and Sopian, K. (2009). Fitting of statistical distributions to wind speed data in Malaysia. European Journal of Scientific Research,26: 6-12.