You are here

Home » Content

WIND SPEED MODELLING USING INVERSE WEIBULL DISTRUBITION: A CASE STUDY FOR BİLECİK, TURKEY

Journal Name:

  • International Journal of Energy Applications and Technologies

Publication Year:

  • 2016

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Wind speed modelling plays a critical role in wind related engineering studies. Frequency distribution of wind speed can be displayed different distributions such as Gamma, lognormal, Rayleigh and Weibull. Weibull distribution is used to model of many regions of the world wind speed in recent year. In this paper, wind speed potential analysis realized using Inverse Weibull Distribution (IWD) for Bilecik, Turkey. Maximum likelihood method for parameter estimation used for wind speed modelling analysis. All analysis is carried out by Matrix Laboratory (MATLAB) programming language. Monthly and yearly wind speeds are modeled by Inverse Weibull distribution. Accuracy of the modelling is evaluated in terms of Root Mean Square Error (RMSE).
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-wind-speed-modelling-using-inverse-weibull-distrubition-case-study-bilecik-turkey.pdf
  • 2
55
59
  • English

REFERENCES

References: 

[1] Akdağ, S.A., and Dinler, A., “A new method to estimate Weibull parameters for wind energy applications”, Energy Conversion and Management, vol. 50(7), pp. 1761-1766, 2009.
[2] Akgül, F.G., Şenoğlu, B., and Arslan, T., “An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution”, Energy Conversion and Management, vol. 114, pp. 234-240, 2016.
[3] Bardsley, W. E., (1980). “Note on the use of the inverse Gaussian distribution for wind energy applications”, Journal of Applied Meteorology, vol. 19(9), pp. 1126-1130, 1980.
[4] Carta, J.A., Ramirez, P., and Velazquez, S., “A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands”, Renewable and Sustainable Energy Reviews, vol. 13(5), pp. 933-955, 2009.
[5] Garcia, A., Torres, J.L., Prieto, E., and De Francisco, A., “Fitting wind speed distributions: a case study”, Solar Energy, vol. 62(2), pp. 139-144, 1998.
[6] Guttman, N.B., Hosking, J.R.M., and Wallis, J.R., “Regional precipitation quantile values for the continental United States computed from L-
moments”, Journal of Climate, vol. 6(12), pp. 2326-2340, 1993.
[7] Justus, C.G., Hargraves, W.R., Mikhail, A., and Graber, D., “Methods for estimating wind speed frequency distributions”, Journal of Applied meteorology, vol. 17(3), pp. 350-353, 1978.
[8] Jaramillo, O.A., and Borja, M.A., “Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case”, Renewable Energy, vol. 29(10), pp. 1613-1630, 2004.
[9] Justus, C.G., Hargraves, W.R., and Yalcin, A., “Nationwide assessment of potential output from wind-powered generators”, 1976.
[10] Kaminsky, F.C., “Four probability densities/log-normal, gamma, Weibull, and Rayleigh/and their application to modelling average hourly wind speed”, In International Solar Energy Society, Annual Meeting, vol. 1, pp. 19-6, 1977.
[11] Kiss, P., and Jánosi, I.M.,”Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe,” Energy Conversion and Management, vol. 49(8), pp. 2142-2151, 2008.
[12] Kurban, M., Kantar, Y. M., and Hocaoğlu, F. O., “Rüzgar Enerjisi Potansiyelinin Araştırılmasında Weibull ve Rayleıgh Dağılımının Kullanılması”, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 10(1), pp. 14-21, 2006.
[13] Kurban, M., Hocaoğlu, F. O., and Kantar, Y. M., “Rüzgar Enerjisi Potansiyelinin Tahmininde Kullanılan İki Farklı İstatistiksel Dağılımın Karşılaştırmalı Analizi”, Pamukkale University Journal Of Engineering Sciences, vol. 13(1), 2011.
[14] Luna, R.E., and Church, H.W., ”Estimation of long-term concentrations using a “universal” wind speed distribution”, Journal of Applied Meteorology, vol. 13(8), pp. 910-916, 1974.
[15] Lysen, E.H., “Introduction to wind energy”, Consultancy services wind energy developing countries (CWD), 1982.
[16] Morgan, E.C., Lackner, M., Vogel, R.M., and Baise, L.G., “Probability distributions for offshore wind speeds”, Energy Conversion and Management, vol. 52(1), pp. 15-26, 2011.
[17] Pishgar-Komleh, S.H., Keyhani, A., and Sefeedpari, P., “Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)”, Renewable and Sustainable Energy Reviews, vol. 42, p.p. 313-322, 2015.
[18] Rosen, K., Van Buskirk, R., and Garbesi, K., “Wind energy potential of coastal Eritrea: an analysis of sparse wind data”, Solar Energy, vol. 66(3), pp. 201-213, 1999.
[19] Stedinger, J.R., “Fitting log normal distributions to hydrologic data”, Water Resources Research, vol. 16(3), pp. 481-490, 1980.
E Dokur et al / International Journal of Energy Applications and Technologies / 3(2) 55 – 59, 2016
59
[20] Stevens, M.J.M., and Smulders, P.T., “The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes”, Wind Engineering, vol. 3, pp. 132-145, 1979.
[21] Sherlock, R.H., “Analyzing winds for frequency and duration”, Meteorological Monographs, vol. 4, pp. 72-79, 1951.
[22] Takle, E.S., and Brown, J.M., “Note on the use of Weibull statistics to characterize wind-speed data”, Journal of Applied Meteorology, vol. 17(4), pp. 556-559, 1978.
[23] Van der Auwera, L., De Meyer, F., and Malet, L.M., “The use of the Weibull three-parameter model for estimating mean wind power densities”, Journal of Applied Meteorology, vol. 19(7), pp. 819-825, 1980.
[24] Vogel, R.M., McMahon, T.A., and Chiew, F.H., “Floodflow frequency model selection in Australia”, Journal of Hydrology, vol. 146, pp. 421-449, 1993.

Thank you for copying data from http://www.arastirmax.com