A statistical approach to estimate the wind speed distribution: The case of gelibolu region

Wind energy is renewable and environment friendly. It is an alternative clear energy source compared to the fossil fuels that pollute the lower layer of atmosphere. The most important parameter of the wind energy is the wind speed. Statistical methods are useful for estimating wind speed because it is a random phenomena. For this reason, wind speed probabilities can be estimated by using probability distributions. An accurate determination of probability distribution for wind speed values is very important in evaluating wind speed energy potential of a region. In this study, first, we tried to determine appropriate theoretical pdf (probability density function) by comparing 10 pdf for the wind speed data measured for Gelibolu region. In determining proper pdf , an approach consisting of 3 goodness of fit tests and fitted graphics have been used.

Rüzgar hızı dağılımının tahmin edilmesi için istatistiksel bir yaklaşım: Gelibolu bölgesi örneği

Rüzgar enerjisi yenilenebilir ve çevre dostu bir enerjidir. Rüzgar enerjisi atmosferi kirleten fosil yakıtlarıyla kıyaslandığında, temiz enerji kaynağı için bir alternatiftir. Rüzgar enerjisinin potansiyelinin en önemli parametresi rüzgar hızıdır. Rüzgar hızı rassal olay olarak tanımlandığı için rüzgar hız tahminlerinde istatistiksel yöntemleri kullanmak yararlıdır. Bu nedenle rüzgar hızı, olasılık dağılımları kullanılarak tahmin edilebilir. Bir bölgenin rüzgar hızı enerji potansiyelinin değerlendirilmesi ve tahmini için geçerli bir olasılık dağılımının belirlenmesi çok önemlidir. Bu çalışmada Gelibolu bölgesinde ölçülen rüzgar hızı verilerini temsil edebilecek uygun bir olasılık dağılımı 10 dağılım karşılaştırılarak belirlenmeye çalışılmıştır. Bu amaçla üç uyum iyiliği testi ve grafik yöntemi kullanılmıştır.

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ANDERSON, T.W., DARLING, D.A. (1954). A Test of Goodness of Fit, Journal of American Statistics Association, pp. 765-767.

ARAS, H., YILMAZ, V., ÇELİK, H.E. (2003). Estimation of Monthly Wind Speeds of Eskişehir,Turkey. The First International Exergy, Energy and Environment Symposium, Hotel Princess, Izmir, Turkey 13-17 July.

AUWERA V., MEYER L.F., MALET LM. (1980). The use of the Weibull three parameter model for estimating mean wind power densities. Journal of Apply Meteorology, vol.19,pp. 819–825.

BIVONA, S., BURLON, R., LEONE C. (2003). Hourly wind speed analysis in Sicily. Renewable Energy, vol. 28, pp. 1371-1385.

ÇELİK, A.N. (2003) A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy, vol.29, no.4, pp.593–604.

DORVLO, S.S. A. (2002) Estimating wind speed distribution. Energy Conversion and Management, vol.43, pp. 2311-2318.

GUHA, R., (2004). Statistic lecture notes. Statistical definitions, [Online] Available at: _____http://cheminfo.informatics.indiana.edu/~rguha/writing/notes/stats/node11.html>, [ Erişim tarihi 26.04.2007].

GUPTA B.K. (1986) Weibull parameters for annual and monthly wind speed distributions for five locations in India. Solar Energy, vol.37, pp. 469–71.

LAW A.M. and KELTON W.D. (1991) Simulation Modeling and Analysis. McGraw-Hill. LUN, I.Y.F., LAN J.C. (2000) A study of Weibull parameters using long-term wind observations. Renewable Energy, vol.20, pp.145–53.

OZTOPAL, A, SAHIN, A.D., SEN Z. (200) On the regional wind energy potential of Turkey. Energy, vol.25, pp.189-200.

PASHARDES, S., CHRISTOFIDES, C. (1995) Statistical analysis of wind speed and direction in Cyprus. Solar Energy, vol. 55, no.5, pp.405-414.

Published by the General Directorate of Turkish State Meteorological Service and the General Directorate of Electrical Power Resources Survey Administration, Data Bank, Ankara, Turkey, 2003.

RAMIREZ, P., CARTA, J.A. (2005) Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study. Energy Conversion and Management, vol.46, pp. 2419–2438.

REHMAN, S., HALAWANI T.O., HUSAIN, T. (1994) Weibull parameters for wind speed distribution in Saudi Arabia. Solar Energy, vol.53, pp.473–9.

SEGURO, J.V., LAMBERT, T.W. (200) Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics, vol.85, pp. 75-84.

STEVENS, M., SMULDERS, P.T. (1979). The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Energy ,pp. 132-145.

TOURE, S. (2005) Investigations on the Eigen-coordinates method for the 2-parameter Weibull distribution of wind speed. Renewable Energy, vol.30, pp.511-521.

TULLER, S.E., BRETT, A.C. (1984) The characteristics of wind velocity that favor the fitting of a Weibull distribution in wind speed analysis. Journal of Apply Meteorology, vol.23, pp.124-34.

SEN Z., SAHIN A.D (1998) Regional wind energy evaluation in some parts of Turkey. Journal of Wind Engineering and Industrial Aerodynamics, vol.74-76, pp.345-353.

ULGEN, K., HEPBASLI, A. (2002) Determination of Weibull parameters for wind energy analysis of İzmir, Turkey. International Journal of Energy Research., vol.26, no.6, pp.495–506.

YILMAZ, V., ÇELİK, H.E. (2004) The estimation of earthquake risk in Eskişehir, Turkey. Anadolu University Journal of Science and Technology, vol.5, no.2, pp.279-283

YILMAZ, V., ARAS,H., ARAS, N., ÇELIK,H.E. (2004) Estimation of Monthly Wind Speed By Using Least Squares & Exponantial Smoothing Technique. An International Symposium Cappadocia-Urgup, Turkey,July 14-16.

XI, M.Y., TANG, A. (2002) Modified Weibull Extension With Bathtub-Shaped Failure Rate Function. Reliability Engineering and System Safety, vol.76, pp.279-285.

WEISSER, D. (2003) A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function. Renewable Energy, vol. 28 ,pp. 1803–1812.

ZHOU, W., YANG, H., FANG Z. (2006) Wind power potential and characteristic analysis of the Pearl River Delta region, China. Renewable Energy, vol.31, pp. 739-753.