Research Article
BibTex RIS Cite
Year 2024, Volume: 37 Issue: 1, 237 - 247, 01.03.2024
https://doi.org/10.35378/gujs.1092617

Abstract

References

  • [1] Sadorsky, P., “WE for sustainable development: Driving factors and future outlook”, Journal of Cleaner Production, 289, 125779, (2021).
  • [2] Al-Dousari, A., Al-Nassar, W., Al-Hemoud, A., Alsaleh, A., Ramadan, A., Al-Dousari, N., Ahmed, M., “Solar and WE: challenges and solutions in desert regions”, Energy, 176: 184-194, (2019).
  • [3] Agalar, S., Kaplan, Y. A., “Design of a custom power park for wind turbine system and analysis of the system performance under power quality disturbances”, IET Renewable Power Generation, 9(8): 943-953, (2015).
  • [4] Kaplan, Y. A., “Comparison of the Performance of the Methods Used to Find the Weibull Parameters at Different Heights”, Arabian Journal for Science and Engineering, 46(12): 12145-12153, (2021).
  • [5] Wang, W., Okaze, T., “Statistical analysis of low-occurrence strong wind speeds at the pedestrian level around a simplified building based on the Weibull distribution”, Building and Environment, 209: 108644, (2022).
  • [6] Safari, M. A. M., Masseran, N., Majid, M. H. A., “Wind energy potential assessment using Weibull distribution with various numerical estimation methods: a case study in Mersing and Port Dickson, Malaysia”, Theoretical and Applied Climatology, 148(3-4): 1085-1110, (2022).
  • [7] Hussain, I., Haider, A., Ullah, Z., Russo, M., Casolino, G. M., Azeem, B., “Comparative Analysis of Eight Numerical Methods Using Weibull Distribution to Estimate Wind Power Density for Coastal Areas in Pakistan”, Energies, 16(3): 1515, (2023).
  • [8] Kaplan, A. G., “A new approach based on moving least square method for calculating the Weibull coefficients”, Environmental Progress & Sustainable Energy, 41(4): e13934, (2022).
  • [9] Soulouknga, M. H., Doka, S. Y., Revanna, N., Djongyang, N., Kofane, T. C., “Analysis of WS data and WE potential in Faya-Largeau, Chad, using Weibull distribution”, Renewable Energy, 121: 1-8, (2018).
  • [10] Kaplan, Y. A., “Determination of Weibull parameters by different numerical methods and analysis of wind power density in Osmaniye, Turkey”, Scientia Iranica, 24(6): 3204-3212, (2017).
  • [11] Katinas, V., Marčiukaitis, M., Gecevičius, G., Markevičius, A., “Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania”, Renewable Energy, 113: 190-201, (2017).
  • [12] Kantar Y.M., Usta I., “Analysis of WS distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function”, Energy Convers Manage, 49: 962–973, (2008).
  • [13] Freitas de Andrade C., Maia Neto H. F., Costa Rocha P. A., Vieira da Silva M. E., “An efficiency comparison of numerical methods for determining Weibull parameters for WE applications: A new approach applied to the northeast region of Brazil”, Energy Convers Manage, 86 (10): 801–808, (2014).
  • [14] Gokcek M., Bayulken A., Bekdemir S., “Investigation of wind characteristics and WE potential in Kirklareli, Turkey”, Renewable Energy, 32: 1739–1752, (2007).
  • [15] Usta, I., “An innovative estimation method regarding Weibull parameters for WE applications”, Energy, 106: 301-314 (2016).
  • [16] Shoaib, M., Siddiqui, I., Amir, Y. M., Rehman, S. U., “Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function”, Renewable and Sustainable Energy Reviews, 70: 1343-1351, (2017).
  • [17] Akdağ, S. A., Dinler, A., “A new method to estimate Weibull parameters for WE applications”, Energy conversion and management, 50(7): 1761-1766, (2009).
  • [18] Khan, J. K., Ahmed, F., Uddin, Z., Iqbal, S. T., Jilani, S. U., Siddiqui, A., Aijaz, A., “Determination of Weibull Parameter by Four Numerical Methods and Prediction of WS in Jiwani (Balochistan) ”, Journal of Basic and Applied Sciences, 11: 62-68, (2015).
  • [19] Islam, M.R., Saidur, R., Rahim, N.A., “Assessment of WE potentiality at Kudat and Labuan, Malaysia using Weibull distribution function”, Energy, 36 (2): 985–992, (2011).
  • [20] Chang, T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for WE application”, Applied Energy, 88: 272–282, (2011).
  • [21] Chaurasiya, P. K., Ahmed, S., Warudkar, V., “Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument”, Alexandria Engineering Journal, 57(4): 2299-2311, (2017).
  • [22] Usta, I., Arik, I., Yenilmez, I., Kantar, Y. M., “A new estimation approach based on moments for estimating Weibull parameters in wind power applications”, Energy Conversion and Management, 164: 570-578, (2018).
  • [23] Kaoga, D. K., Sergeb, D. Y., Raidandic, D., Djongyangd, N., “Performance Assessment of Two-parameter Weibull Distribution Methods for WE Applications in the District of Maroua in Cameroon”, International Journal of Sciences, Basic and Applied Research (IJSBAR), 17(1): 39-59, (2014).
  • [24] Morgan, E. C., Lackner, M., Vogal, R. M., Baise, L. G., “Probability distributions of offshore wind speeds”, Energy Conversion and Management, 52: 15–26, (2011).
  • [25] TEIAS, Turkish electricity transmission company, (http://www.teias.gov.tr). Access date:15.03.2022
  • [26] Mohammadi, K., Mostafaeipour, A., “Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran”, Energy Conversion and Management, 65: 463-470, (2013).
  • [27] Gokcek, M., Bayulken, A., Bekdemir S., “Investigation of wind characteristics and WE potential in Kirklareli, Turkey”, Renewable Energy, 32: 1739–1752, (2007).
  • [28] Aries, N., Boudia, S. M., Ounis, H., “Deep assessment of WS distribution models: A case study of four sites in Algeria”, Energy Conversion and Management, 155: 78-90, (2018).
  • [29] Talha A., Bulut, Y. M., Yavuz, A., “Comparative study of numerical methods for determining Weibull parameters for WE potential ”, Renewable and Sustainable Energy Reviews, 40: 820-825, (2014).

Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites

Year 2024, Volume: 37 Issue: 1, 237 - 247, 01.03.2024
https://doi.org/10.35378/gujs.1092617

Abstract

In this study, the compliance of the Weibull Distribution Function (WDF) and actual wind data (WD) from three different locations were investigated. The coefficients of the WDF were calculated using the Maximum Likelihood Method (MLM) in the Adana, Osmaniye, and Hatay regions. The main purpose of this study is to observe the performances of the MLM in determining the coefficients of the WDF in different regions in different years and to examine the success of this method in estimating the mean wind power and speed of the determined regions. The performance of the indicated approach in all three selected locations was evaluated using the Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Percentage Error (MPE). Also wind power densities were estimated for all three regions, which are one of the most essential metrics for estimating a region's wind energy (WE) potential. WDF power densities were estimated and compared to real wind power densities generated from measured WD for three different places. The performance of the method described in this paper was investigated in depth in various places with varying geographic characteristics. In addition, in the same years, the performance of the chosen method was evaluated in detail in three distinct places, and it was seen how geographical factors affected the method's performance.

References

  • [1] Sadorsky, P., “WE for sustainable development: Driving factors and future outlook”, Journal of Cleaner Production, 289, 125779, (2021).
  • [2] Al-Dousari, A., Al-Nassar, W., Al-Hemoud, A., Alsaleh, A., Ramadan, A., Al-Dousari, N., Ahmed, M., “Solar and WE: challenges and solutions in desert regions”, Energy, 176: 184-194, (2019).
  • [3] Agalar, S., Kaplan, Y. A., “Design of a custom power park for wind turbine system and analysis of the system performance under power quality disturbances”, IET Renewable Power Generation, 9(8): 943-953, (2015).
  • [4] Kaplan, Y. A., “Comparison of the Performance of the Methods Used to Find the Weibull Parameters at Different Heights”, Arabian Journal for Science and Engineering, 46(12): 12145-12153, (2021).
  • [5] Wang, W., Okaze, T., “Statistical analysis of low-occurrence strong wind speeds at the pedestrian level around a simplified building based on the Weibull distribution”, Building and Environment, 209: 108644, (2022).
  • [6] Safari, M. A. M., Masseran, N., Majid, M. H. A., “Wind energy potential assessment using Weibull distribution with various numerical estimation methods: a case study in Mersing and Port Dickson, Malaysia”, Theoretical and Applied Climatology, 148(3-4): 1085-1110, (2022).
  • [7] Hussain, I., Haider, A., Ullah, Z., Russo, M., Casolino, G. M., Azeem, B., “Comparative Analysis of Eight Numerical Methods Using Weibull Distribution to Estimate Wind Power Density for Coastal Areas in Pakistan”, Energies, 16(3): 1515, (2023).
  • [8] Kaplan, A. G., “A new approach based on moving least square method for calculating the Weibull coefficients”, Environmental Progress & Sustainable Energy, 41(4): e13934, (2022).
  • [9] Soulouknga, M. H., Doka, S. Y., Revanna, N., Djongyang, N., Kofane, T. C., “Analysis of WS data and WE potential in Faya-Largeau, Chad, using Weibull distribution”, Renewable Energy, 121: 1-8, (2018).
  • [10] Kaplan, Y. A., “Determination of Weibull parameters by different numerical methods and analysis of wind power density in Osmaniye, Turkey”, Scientia Iranica, 24(6): 3204-3212, (2017).
  • [11] Katinas, V., Marčiukaitis, M., Gecevičius, G., Markevičius, A., “Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania”, Renewable Energy, 113: 190-201, (2017).
  • [12] Kantar Y.M., Usta I., “Analysis of WS distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function”, Energy Convers Manage, 49: 962–973, (2008).
  • [13] Freitas de Andrade C., Maia Neto H. F., Costa Rocha P. A., Vieira da Silva M. E., “An efficiency comparison of numerical methods for determining Weibull parameters for WE applications: A new approach applied to the northeast region of Brazil”, Energy Convers Manage, 86 (10): 801–808, (2014).
  • [14] Gokcek M., Bayulken A., Bekdemir S., “Investigation of wind characteristics and WE potential in Kirklareli, Turkey”, Renewable Energy, 32: 1739–1752, (2007).
  • [15] Usta, I., “An innovative estimation method regarding Weibull parameters for WE applications”, Energy, 106: 301-314 (2016).
  • [16] Shoaib, M., Siddiqui, I., Amir, Y. M., Rehman, S. U., “Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function”, Renewable and Sustainable Energy Reviews, 70: 1343-1351, (2017).
  • [17] Akdağ, S. A., Dinler, A., “A new method to estimate Weibull parameters for WE applications”, Energy conversion and management, 50(7): 1761-1766, (2009).
  • [18] Khan, J. K., Ahmed, F., Uddin, Z., Iqbal, S. T., Jilani, S. U., Siddiqui, A., Aijaz, A., “Determination of Weibull Parameter by Four Numerical Methods and Prediction of WS in Jiwani (Balochistan) ”, Journal of Basic and Applied Sciences, 11: 62-68, (2015).
  • [19] Islam, M.R., Saidur, R., Rahim, N.A., “Assessment of WE potentiality at Kudat and Labuan, Malaysia using Weibull distribution function”, Energy, 36 (2): 985–992, (2011).
  • [20] Chang, T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for WE application”, Applied Energy, 88: 272–282, (2011).
  • [21] Chaurasiya, P. K., Ahmed, S., Warudkar, V., “Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument”, Alexandria Engineering Journal, 57(4): 2299-2311, (2017).
  • [22] Usta, I., Arik, I., Yenilmez, I., Kantar, Y. M., “A new estimation approach based on moments for estimating Weibull parameters in wind power applications”, Energy Conversion and Management, 164: 570-578, (2018).
  • [23] Kaoga, D. K., Sergeb, D. Y., Raidandic, D., Djongyangd, N., “Performance Assessment of Two-parameter Weibull Distribution Methods for WE Applications in the District of Maroua in Cameroon”, International Journal of Sciences, Basic and Applied Research (IJSBAR), 17(1): 39-59, (2014).
  • [24] Morgan, E. C., Lackner, M., Vogal, R. M., Baise, L. G., “Probability distributions of offshore wind speeds”, Energy Conversion and Management, 52: 15–26, (2011).
  • [25] TEIAS, Turkish electricity transmission company, (http://www.teias.gov.tr). Access date:15.03.2022
  • [26] Mohammadi, K., Mostafaeipour, A., “Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran”, Energy Conversion and Management, 65: 463-470, (2013).
  • [27] Gokcek, M., Bayulken, A., Bekdemir S., “Investigation of wind characteristics and WE potential in Kirklareli, Turkey”, Renewable Energy, 32: 1739–1752, (2007).
  • [28] Aries, N., Boudia, S. M., Ounis, H., “Deep assessment of WS distribution models: A case study of four sites in Algeria”, Energy Conversion and Management, 155: 78-90, (2018).
  • [29] Talha A., Bulut, Y. M., Yavuz, A., “Comparative study of numerical methods for determining Weibull parameters for WE potential ”, Renewable and Sustainable Energy Reviews, 40: 820-825, (2014).
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Energy Systems Engineering
Authors

Alper Kaplan 0000-0003-1067-110X

Early Pub Date August 23, 2023
Publication Date March 1, 2024
Published in Issue Year 2024 Volume: 37 Issue: 1

Cite

APA Kaplan, A. (2024). Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites. Gazi University Journal of Science, 37(1), 237-247. https://doi.org/10.35378/gujs.1092617
AMA Kaplan A. Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites. Gazi University Journal of Science. March 2024;37(1):237-247. doi:10.35378/gujs.1092617
Chicago Kaplan, Alper. “Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites”. Gazi University Journal of Science 37, no. 1 (March 2024): 237-47. https://doi.org/10.35378/gujs.1092617.
EndNote Kaplan A (March 1, 2024) Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites. Gazi University Journal of Science 37 1 237–247.
IEEE A. Kaplan, “Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites”, Gazi University Journal of Science, vol. 37, no. 1, pp. 237–247, 2024, doi: 10.35378/gujs.1092617.
ISNAD Kaplan, Alper. “Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites”. Gazi University Journal of Science 37/1 (March 2024), 237-247. https://doi.org/10.35378/gujs.1092617.
JAMA Kaplan A. Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites. Gazi University Journal of Science. 2024;37:237–247.
MLA Kaplan, Alper. “Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites”. Gazi University Journal of Science, vol. 37, no. 1, 2024, pp. 237-4, doi:10.35378/gujs.1092617.
Vancouver Kaplan A. Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites. Gazi University Journal of Science. 2024;37(1):237-4.