Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 35 Sayı: 2, 765 - 774, 01.06.2022
https://doi.org/10.35378/gujs.753789

Öz

Kaynakça

  • [1] Khan, J. K., Shoaib, M., Uddin, Z., Siddiqui, I. A., Aijaz, A., Siddiqui, A. A. and Hussain, E., “Comparison of wind energy potential for coastal locations: Pasni and Gwadar”, Journal of Basic & Applied Sciences, 11: 211–216, (2015).
  • [2] 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, 13(5): 933–955, (2009).
  • [3] Akpinar, S. and Akpinar, E. K., “Estimation of wind energy potential using finite mixture distribution models”, Energy Conversion and Management, 50(4): 877–884, (2009).
  • [4] Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M. and Abbaszadeh, R., “An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran”, Energy, 35(1): 188–201, (2010).
  • [5] Akdağ, S. A., Bagiorgas, H. S. and Mihalakakou, G., “Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean”, Applied Energy, 87(8): 2566–2573, (2010).
  • [6] Chang, T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application”, Applied Energy, 88(1): 272–282, (2011).
  • [7] Usta, I. and Kantar, Y. M., “Analysis of some flexible families of distributions for estimation of wind speed distributions”, Applied Energy, 89(1): 355–367, (2012).
  • [8] Chellali, F., Khellaf, A., Belouchrani, A. and Khanniche, R., “A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria”, Renewable and Sustainable Energy Reviews, 16(1): 379–385, (2012).
  • [9] Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F. and da Silva, M. E. V., “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil”, Applied Energy, 89(1): 395–400, (2012).
  • [10] Kantar, Y. M., Usta, I., Arik, I. and Yenilmez, I., “Wind Speed Analysis Using the Extended Generalized Lindley Distribution”, Renewable Energy, 30: 1-7, (2017).
  • [11] Shoaib, M., Dar, I. S., Ahsan-ul-Haq, M. and Usman, R. M. “A sustainable generalization of inverse Lindley distribution for wind speed analysis in certain regions of Pakistan”, Modeling Earth Systems and Environment, 1-13, (2021). [12] Ahsan-ul-Haq, M., Rao, G. S., Albassam, M. and Aslam, M. “Marshall–Olkin power Lomax distribution for modeling of wind speed data“, Energy Reports, 6: 1118-1123, (2020).
  • [13] De Andrade, C. F., Neto, H. F. M., Rocha, P. A. C. and da 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”, Energy Conversion and Management, 86: 801–808, (2014).
  • [14] Bilir, L., Imir, M., Devrim, Y. and Albostan, A., “An investigation on wind energy potential and small scale wind turbine performance at İncek region–Ankara, Turkey”, Energy Conversion and Management, 103: 910–923, (2015).
  • [15] Arslan, T., Acitas, S. and Senoglu, B., “Generalized Lindley and Power Lindley distributions for modeling the wind speed data”, Energy Conversion and Management, 152(8): 300–311, (2017).
  • [16] 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, 114: 234–240, (2016).
  • [17] Alavi, O., Mohammadi, K. and Mostafaeipour, A., “Evaluating the suitability of wind speed probability distribution models : A case of study of east and southeast parts of Iran”, Energy Conversion and Management, 119: 101–108, (2016).
  • [18] Shin, J. Y., Ouarda, T. B. and Lee, T., “Heterogeneous mixture distributions for modeling wind speed, application to the UAE”, Renewable Energy, 91: 40–52, (2016).
  • [19] Hossain, J., Sharma, S. and Kishore, V. V. N., “Multi-peak Gaussian fit applicability to wind speed distribution”, Renewable and Sustainable Energy Reviews, 34: 483–490, (2014).
  • [20] Morgan, E. C., Lackner, M., Vogel, R. M. and Baise, L. G., “Probability distributions for offshore wind speeds”, Energy Conversion and Management, 52(1): 15–26, (2011).
  • [21] ul Haq, M. A., Chand, S., Sajjad, M. Z. and Usman, R. M., “Evaluating the suitability of two parametric wind speed distributions: a case study from Pakistan“, Modeling Earth Systems and Environment, 1-9, (2020).
  • [22] Lindley, D. V., “Fiducial distributions and Bayes’ theorem”, Journal of the Royal Statistical Society: Series B, 102–107, (1958).
  • [23] Nadarajah, S., Bakouch, H. S. and Tahmasbi, R., “A generalized Lindley distribution”, Sankhya B, 73(2): 331–359, (2011).
  • [24] Ghitany, M. E., Al-Mutairi, D. K., Balakrishnan, N. and Al-Enezi, L. J., “Power Lindley distribution and associated inference”, Computational Statistics & Data Analysis, 64: 20–33, (2013).
  • [25] Bhati, D., Malik, M. and Jose, K. K., “A new 3-parameter extension of generalized Lindley distribution”, arXiv Prepr. arXiv1601.01045, (2016).
  • [26] Elbatal, I., Diab, L. S. and Elgarhy, M., “Exponentiated quasi Lindley distribution”, International Journal of Reliability and Applications, 17(1): 1–19, (2016).

Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution

Yıl 2022, Cilt: 35 Sayı: 2, 765 - 774, 01.06.2022
https://doi.org/10.35378/gujs.753789

Öz

The wind energy potential of a specified area can be estimated using wind speed distribution. In this study, the selection of probability density functions is used to model wind speed data recorded at two stations in Pakistan. The suitability of fitted distributions is evaluated using the goodness of fit criterion, power density error, log-likelihood, root mean square error, coefficient of determination, AIC, and BIC. The wind speed data are obtained from two coastal regions of Pakistan at 10m/s average rate for session 2017-2018. Findings indicated that the extended generalized Lindley distribution provide generally the best fit to the wind speed data for both stations. However, it is also observed that power Lindley and extended generalized Lindley distributions have better performance based on power density error criteria in Gwadar and Haripur, respectively. 

Kaynakça

  • [1] Khan, J. K., Shoaib, M., Uddin, Z., Siddiqui, I. A., Aijaz, A., Siddiqui, A. A. and Hussain, E., “Comparison of wind energy potential for coastal locations: Pasni and Gwadar”, Journal of Basic & Applied Sciences, 11: 211–216, (2015).
  • [2] 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, 13(5): 933–955, (2009).
  • [3] Akpinar, S. and Akpinar, E. K., “Estimation of wind energy potential using finite mixture distribution models”, Energy Conversion and Management, 50(4): 877–884, (2009).
  • [4] Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M. and Abbaszadeh, R., “An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran”, Energy, 35(1): 188–201, (2010).
  • [5] Akdağ, S. A., Bagiorgas, H. S. and Mihalakakou, G., “Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean”, Applied Energy, 87(8): 2566–2573, (2010).
  • [6] Chang, T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application”, Applied Energy, 88(1): 272–282, (2011).
  • [7] Usta, I. and Kantar, Y. M., “Analysis of some flexible families of distributions for estimation of wind speed distributions”, Applied Energy, 89(1): 355–367, (2012).
  • [8] Chellali, F., Khellaf, A., Belouchrani, A. and Khanniche, R., “A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria”, Renewable and Sustainable Energy Reviews, 16(1): 379–385, (2012).
  • [9] Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F. and da Silva, M. E. V., “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil”, Applied Energy, 89(1): 395–400, (2012).
  • [10] Kantar, Y. M., Usta, I., Arik, I. and Yenilmez, I., “Wind Speed Analysis Using the Extended Generalized Lindley Distribution”, Renewable Energy, 30: 1-7, (2017).
  • [11] Shoaib, M., Dar, I. S., Ahsan-ul-Haq, M. and Usman, R. M. “A sustainable generalization of inverse Lindley distribution for wind speed analysis in certain regions of Pakistan”, Modeling Earth Systems and Environment, 1-13, (2021). [12] Ahsan-ul-Haq, M., Rao, G. S., Albassam, M. and Aslam, M. “Marshall–Olkin power Lomax distribution for modeling of wind speed data“, Energy Reports, 6: 1118-1123, (2020).
  • [13] De Andrade, C. F., Neto, H. F. M., Rocha, P. A. C. and da 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”, Energy Conversion and Management, 86: 801–808, (2014).
  • [14] Bilir, L., Imir, M., Devrim, Y. and Albostan, A., “An investigation on wind energy potential and small scale wind turbine performance at İncek region–Ankara, Turkey”, Energy Conversion and Management, 103: 910–923, (2015).
  • [15] Arslan, T., Acitas, S. and Senoglu, B., “Generalized Lindley and Power Lindley distributions for modeling the wind speed data”, Energy Conversion and Management, 152(8): 300–311, (2017).
  • [16] 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, 114: 234–240, (2016).
  • [17] Alavi, O., Mohammadi, K. and Mostafaeipour, A., “Evaluating the suitability of wind speed probability distribution models : A case of study of east and southeast parts of Iran”, Energy Conversion and Management, 119: 101–108, (2016).
  • [18] Shin, J. Y., Ouarda, T. B. and Lee, T., “Heterogeneous mixture distributions for modeling wind speed, application to the UAE”, Renewable Energy, 91: 40–52, (2016).
  • [19] Hossain, J., Sharma, S. and Kishore, V. V. N., “Multi-peak Gaussian fit applicability to wind speed distribution”, Renewable and Sustainable Energy Reviews, 34: 483–490, (2014).
  • [20] Morgan, E. C., Lackner, M., Vogel, R. M. and Baise, L. G., “Probability distributions for offshore wind speeds”, Energy Conversion and Management, 52(1): 15–26, (2011).
  • [21] ul Haq, M. A., Chand, S., Sajjad, M. Z. and Usman, R. M., “Evaluating the suitability of two parametric wind speed distributions: a case study from Pakistan“, Modeling Earth Systems and Environment, 1-9, (2020).
  • [22] Lindley, D. V., “Fiducial distributions and Bayes’ theorem”, Journal of the Royal Statistical Society: Series B, 102–107, (1958).
  • [23] Nadarajah, S., Bakouch, H. S. and Tahmasbi, R., “A generalized Lindley distribution”, Sankhya B, 73(2): 331–359, (2011).
  • [24] Ghitany, M. E., Al-Mutairi, D. K., Balakrishnan, N. and Al-Enezi, L. J., “Power Lindley distribution and associated inference”, Computational Statistics & Data Analysis, 64: 20–33, (2013).
  • [25] Bhati, D., Malik, M. and Jose, K. K., “A new 3-parameter extension of generalized Lindley distribution”, arXiv Prepr. arXiv1601.01045, (2016).
  • [26] Elbatal, I., Diab, L. S. and Elgarhy, M., “Exponentiated quasi Lindley distribution”, International Journal of Reliability and Applications, 17(1): 1–19, (2016).
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Statistics
Yazarlar

Rana Usman 0000-0003-4263-0652

Muhammad Ahsan-ul-haq 0000-0002-0902-8080

Nurbanu Bursa 0000-0003-3747-5870

Yayımlanma Tarihi 1 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 35 Sayı: 2

Kaynak Göster

APA Usman, R., Ahsan-ul-haq, M., & Bursa, N. (2022). Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution. Gazi University Journal of Science, 35(2), 765-774. https://doi.org/10.35378/gujs.753789
AMA Usman R, Ahsan-ul-haq M, Bursa N. Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution. Gazi University Journal of Science. Haziran 2022;35(2):765-774. doi:10.35378/gujs.753789
Chicago Usman, Rana, Muhammad Ahsan-ul-haq, ve Nurbanu Bursa. “Wind Speed Analysis for Coastal Regions of Pakistan Using Extended Generalized Lindley Distribution”. Gazi University Journal of Science 35, sy. 2 (Haziran 2022): 765-74. https://doi.org/10.35378/gujs.753789.
EndNote Usman R, Ahsan-ul-haq M, Bursa N (01 Haziran 2022) Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution. Gazi University Journal of Science 35 2 765–774.
IEEE R. Usman, M. Ahsan-ul-haq, ve N. Bursa, “Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution”, Gazi University Journal of Science, c. 35, sy. 2, ss. 765–774, 2022, doi: 10.35378/gujs.753789.
ISNAD Usman, Rana vd. “Wind Speed Analysis for Coastal Regions of Pakistan Using Extended Generalized Lindley Distribution”. Gazi University Journal of Science 35/2 (Haziran 2022), 765-774. https://doi.org/10.35378/gujs.753789.
JAMA Usman R, Ahsan-ul-haq M, Bursa N. Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution. Gazi University Journal of Science. 2022;35:765–774.
MLA Usman, Rana vd. “Wind Speed Analysis for Coastal Regions of Pakistan Using Extended Generalized Lindley Distribution”. Gazi University Journal of Science, c. 35, sy. 2, 2022, ss. 765-74, doi:10.35378/gujs.753789.
Vancouver Usman R, Ahsan-ul-haq M, Bursa N. Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution. Gazi University Journal of Science. 2022;35(2):765-74.