Abstract
Bu çalışmada 2012-2016 yılları arasında Bitlis’te ortalama
rüzgâr hızı verileri analiz edilmiştir. Bu yıllar için ortalama rüzgâr hızı tahminleri
Weibull, Gamma ve Lognormal dağılımları ile elde edilmiştir. Bu dağılımların
parametre tahminlerinde En Çok Olabilirlik yöntemi kullanılmıştır.
Kolmogorov-Smirnov uyum iyiliği testi, belirleme katsayısı ve hata kareler
ortalamasının karekökü kriterleri ile en uygun dağılımın belirlenmesi
amaçlanmıştır. MATLAB R2009a'da yazılan program ile rüzgâr hızı verilerinin
değerlendirilmesi sonucunda her bir dağılım için ortalama rüzgâr hızı
tahminlerinin benzer olduğu, buna karşılık Gamma dağılımının Ağustos ayına ait
ortalama rüzgâr hızı değeri (0.15 m/s) ile en düşük standart sapmaya sahip
olduğu belirlenmiştir. 2012-2016 yılları arasındaki ortalama rüzgâr hızı
verilerinin modellenmesinde Gamma dağılımının diğer dağılımlara göre daha
yüksek belirleme katsayısı değerine sahip olduğu görülmüştür. Benzer biçimde en
küçük Kolmogorov-Smirnov uyum iyiliği test istatistiği ve hata kareler
ortalamasının karekökü değeri Gamma dağılımı için elde edilmiştir. Sonuç olarak
2012-2016 yılları arasında Bitlis iline ait rüzgâr hızı verilerinin
modellenmesinde Gamma dağılımının kullanılması önerilmektedir.
References
- [1]. Fyrippis I., Axaopoulos P.J., Panayiotou G. Wind energy potential assessment in Naxos Island, Greece Appl Energ 2010; 87:577-86.
- [2]. Mohammadi K., Alavi O., Mostafaeipour A., Goudarzi N., Jalilvand M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density Energ Convers Manag 2016; 108:322-35.
- [3]. Arslan T., Bulut Y.M, Yavuz A.A. Comparative study of numerical methods for determining weibull parameters for wind energy potential Renew Sustain Energ Rev 2014; 40:820-5.
- [4]. Andrade C.F., Neto H.F.M., Rocha P.A.C., Silva M.E.V. An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil Energ Convers Manag 2014; 86:801-8.
- [5]. Khahro S.F., Tabbassum K., Soomro A.M., Dong L., Liao X. Evaluation of wind power production prospective and weibull parameter estimation methods for Babaurband, Sindh Pakistan Energ Convers Manag 2014; 78:956-67.
- [6]. Werapun W., Tirawanichakul Y., Waewsak J. Comparative study of five methods to estimate weibull parameters for wind speed on Phangan Island, Thailand Energ Proc 2015; 79:976-81.
- [7]. Kurban, M., Kantar, Y.M., Hocaoğlu F.O. Weibull dağılımı kullanılarak rüzgar hız ve güç yoğunluklarının istatistiksel analizi Afyon Kocatepe Univ Fen Müh Bilim Derg 2007; 7: 205-18 (in Turkish).
- [8]. Usta I., Kantar Y.M. Analysis of some flexible families of distributions for estimation of wind speed distributions Appl Energ 2012; 89:355-67.
- [9]. Bilir L., Imir M., Devrim Y., Albostan A. An investigation on wind energy potential and small scale wind turbine performance at Incek region –Ankara, Turkey Energ Convers Manag 2015; 103:910-23.
- [10]. Kantar Y.M., Usta I. Analysis of the upper-truncated weibull distribution for wind speed Energ Convers Manag 2015; 96:81-8.
- [11]. Akgül, F. G., Şenoğlu, B., Arslan, T. An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution Energ Convers Manag 2016; 114:234-40.
- [12]. Carta J.A., Ramirez P., Velazquez S. A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands Renew Sustain Energ Rev 2009; 13:933-55.
- [13]. Brano V.L., Orioli A., Ciulla G., Culotta S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy Renew Energ 2011; 36:1026-39.
- [14]. Morgan, E. C., Lackner, M., Vogel, R. M., Baise, L. G. Probability distributions for offshore wind speeds Energ Convers Manag 2011; 52:15-26.
- [15]. Casella, G., Berger, R.L. Statistical inference, 2nd ed., Duxbury Thomson Learning, USA, 2001.
- [16]. Parajuli, A. A statistical analysis of wind speed and power density based on weibull and rayleigh models of Jumla, Nepal Energ Power Eng 2016; 8:271-82.
- [17]. Ahmed, S.A. Comparative study of four methods for estimating weibull parameters for Halabja, Iraq Int J Phys Sci 2013; 8:186-92.
- [18]. Mert, İ., Karakuş, C. Antakya bölgesinde rüzgâr gücü yoğunluğu ve rüzgâr hızı dağılımı parametrelerinin istatistiksel analizi Politeknik Derg 2015; 18:35-42 (in Turkish).
- [19]. Oner, Y., Ozcira, S., Bekiroglu, N., Senol, I. A comparative analysis of wind power density prediction methods for Çanakkale, Intepe Region, Turkey Renew Sustain Energ Rev 2013; 23:491-502.
- [20]. Islam, M.R., Saidur, R., Rahim, N.A. Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using weibull distribution function Energ 2011; 36:985-92.
- [21]. Pishgar-Komleh, S.H., Keyhani, A., Sefeedpari, P. Wind speed and power density analysis based on weibull and rayleigh distributions (A case study: Firouzkooh County of Iran) Renew Sustain Energ Rev 2015; 42:313-22.
- [22]. Dhunny, A.Z., Lollchund, M.R., Boojhawon, R., Rughooputh, S.D.D.V. Statistical modelling of wind speed data for Mauritius Int J Renew Energ Research 2014; 4:1056-64.
Abstract
In
this study, average wind speed data in Bitlis for the years between 2012 and
2016 is analyzed. Average wind speed estimations for these years are obtained
with the Weibull, Gamma and Lognormal distributions. Maximum Likelihood method
is used in parameter estimation of these distributions. It is aimed that the
most fit distribution is determined with Kolmogorov-Smirnov Goodness of Fit
test, coefficient of determination and root mean square error criteria. As a
result of evaluating the wind speed data with the program written in MATLAB
R2009a, it was determined that average wind speed estimations are similar for
each distribution, but Gamma distribution has the lowest standard deviation
with the average wind speed value in August (0.15 m/s). In modelling of the
average wind speed data between 2012 and 2016, it was seen that Gamma
distribution had higher coefficient of determination compared to the other
distributions. Similarly, the lowest Kolmogorov-Smirnov Goodness of Fit test
statistic and root mean square error value are obtained for Gamma distribution.
As a result, it is recommended that Gamma distribution is used in modelling the
wind speed data of Bitlis between 2012 and 2016.
References
- [1]. Fyrippis I., Axaopoulos P.J., Panayiotou G. Wind energy potential assessment in Naxos Island, Greece Appl Energ 2010; 87:577-86.
- [2]. Mohammadi K., Alavi O., Mostafaeipour A., Goudarzi N., Jalilvand M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density Energ Convers Manag 2016; 108:322-35.
- [3]. Arslan T., Bulut Y.M, Yavuz A.A. Comparative study of numerical methods for determining weibull parameters for wind energy potential Renew Sustain Energ Rev 2014; 40:820-5.
- [4]. Andrade C.F., Neto H.F.M., Rocha P.A.C., Silva M.E.V. An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil Energ Convers Manag 2014; 86:801-8.
- [5]. Khahro S.F., Tabbassum K., Soomro A.M., Dong L., Liao X. Evaluation of wind power production prospective and weibull parameter estimation methods for Babaurband, Sindh Pakistan Energ Convers Manag 2014; 78:956-67.
- [6]. Werapun W., Tirawanichakul Y., Waewsak J. Comparative study of five methods to estimate weibull parameters for wind speed on Phangan Island, Thailand Energ Proc 2015; 79:976-81.
- [7]. Kurban, M., Kantar, Y.M., Hocaoğlu F.O. Weibull dağılımı kullanılarak rüzgar hız ve güç yoğunluklarının istatistiksel analizi Afyon Kocatepe Univ Fen Müh Bilim Derg 2007; 7: 205-18 (in Turkish).
- [8]. Usta I., Kantar Y.M. Analysis of some flexible families of distributions for estimation of wind speed distributions Appl Energ 2012; 89:355-67.
- [9]. Bilir L., Imir M., Devrim Y., Albostan A. An investigation on wind energy potential and small scale wind turbine performance at Incek region –Ankara, Turkey Energ Convers Manag 2015; 103:910-23.
- [10]. Kantar Y.M., Usta I. Analysis of the upper-truncated weibull distribution for wind speed Energ Convers Manag 2015; 96:81-8.
- [11]. Akgül, F. G., Şenoğlu, B., Arslan, T. An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution Energ Convers Manag 2016; 114:234-40.
- [12]. Carta J.A., Ramirez P., Velazquez S. A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands Renew Sustain Energ Rev 2009; 13:933-55.
- [13]. Brano V.L., Orioli A., Ciulla G., Culotta S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy Renew Energ 2011; 36:1026-39.
- [14]. Morgan, E. C., Lackner, M., Vogel, R. M., Baise, L. G. Probability distributions for offshore wind speeds Energ Convers Manag 2011; 52:15-26.
- [15]. Casella, G., Berger, R.L. Statistical inference, 2nd ed., Duxbury Thomson Learning, USA, 2001.
- [16]. Parajuli, A. A statistical analysis of wind speed and power density based on weibull and rayleigh models of Jumla, Nepal Energ Power Eng 2016; 8:271-82.
- [17]. Ahmed, S.A. Comparative study of four methods for estimating weibull parameters for Halabja, Iraq Int J Phys Sci 2013; 8:186-92.
- [18]. Mert, İ., Karakuş, C. Antakya bölgesinde rüzgâr gücü yoğunluğu ve rüzgâr hızı dağılımı parametrelerinin istatistiksel analizi Politeknik Derg 2015; 18:35-42 (in Turkish).
- [19]. Oner, Y., Ozcira, S., Bekiroglu, N., Senol, I. A comparative analysis of wind power density prediction methods for Çanakkale, Intepe Region, Turkey Renew Sustain Energ Rev 2013; 23:491-502.
- [20]. Islam, M.R., Saidur, R., Rahim, N.A. Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using weibull distribution function Energ 2011; 36:985-92.
- [21]. Pishgar-Komleh, S.H., Keyhani, A., Sefeedpari, P. Wind speed and power density analysis based on weibull and rayleigh distributions (A case study: Firouzkooh County of Iran) Renew Sustain Energ Rev 2015; 42:313-22.
- [22]. Dhunny, A.Z., Lollchund, M.R., Boojhawon, R., Rughooputh, S.D.D.V. Statistical modelling of wind speed data for Mauritius Int J Renew Energ Research 2014; 4:1056-64.
Details
| Journal Section | Natural Sciences |
|---|---|
| Authors | |
| Publication Date | December 8, 2017 |
| Submission Date | March 7, 2017 |
| Acceptance Date | October 18, 2017 |
| Published in Issue | Year 2017Volume: 38 Supplement Issue 4 |
