Modeling of wind speed using differential evolution: Istanbul case

Abstract

Over the years, increasing energy demands with the growth of the population and the development of technology have caused more fossil fuel consumption. Besides, environmental pollution and climate change, which are vital importance for humanity, are encountered. In order to avoid these dangerous situations, people have started to turn to clean and renewable energy sources such as wind energy. Due to the rapid development of such situations, it is very important to obtain information on the determination of the regions where wind energy facility will be installed and the characteristics of the wind speed. Wind power estimation can be made through various statistical distributions used to explain the characteristics of wind speed data. Rayleigh, Weibull, Nakagami, Gamma, Logistic, Loglogistic, Lognormal and Burr Type XII distributions, which are frequently used in the wind energy literature, are discussed in this study and the performances of the specified distributions are compared through the data sets obtained from the stations in Istanbul from Marmara region. One of the most preferred methods in estimation problems is the maximum likelihood method, and a differential evolution algorithm is proposed for ML estimation of the parameters of the distributions examined in the study. In addition, various model selection criteria are also utilized to determine the distribution that best fits the wind speed data.

Keywords

• Differential Evolution
• Maximum Likelihood
• Wind Energy

References

1. REFERENCES
2. [1] World Wind Energy Association. Worldwide Wind Capacity Reaches 744 Gigawatts – An Unprecedented 93 Gigawatts added in 2020. Available at: https://old.wwindea.org/worldwide-wind-capacity-reaches-744-gigawatts/. Accessed on May 13, 2024.
3. [2] Altan A, Karasu S, Zio E. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput 2021;100:106996. [CrossRef]
4. [3] Kantar YM, Usta I, Arik I, Yenilmez I. Wind speed analysis using the Extended Generalized Lindley Distribution. Renew Energy 2018;118:1024–1030. [CrossRef]
5. [4] Tuller SE, Brett AC. The characteristics of wind velocity that favor the fitting of a Weibull distribution in wind speed analysis. J Clim Appl Meteorol 1984;23:124–134. [CrossRef]
6. [5] Akda SA, Guler O. Calculation of wind energy potential and economic analysis by using weibull distribution - A case study from Turkey. Part 1: Determination of weibull parameters. Energy Sources Part B: Econ Plan Policy 2009;4:1–8. [CrossRef]
7. [6] Ozgur MA, Arslan O, Kose R, Peker KO. Statistical evaluation of wind characteristics in Kutahya, Turkey. Energy Sources Part A: Recov Util Environ Effect 2009;31:1460–1463. [CrossRef]
8. [7] Sharifi F, Hashemi N. An analysis of current and future wind energy gain potential for central Iowa. J Ther Eng 2015;1:245–250. [CrossRef]

9. [8] Jowder FAL. Weibull and Rayleigh distribution functions of wind speeds in Kingdom of Bahrain. Wind Eng 2006;30:1000–1009. [CrossRef]
10. [9] Ohunakin OS. Wind resource evaluation in six selected high altitude locations in Nigeria. Renew Energy 2011;36:3273–3281. [CrossRef]
11. [10] Nawri N, Petersen GN, Bjornsson H, Hahmann AN, Jónasson K, Hasager CB, et al. The wind energy potential of Iceland. Renew Energy 2014;69:190–299. [CrossRef]
12. [11] Karthikeya BR, Negi PS, Srikanth N. Wind resource assessment for urban renewable energy application in Singapore. Renew Energy 2016;87:403–414. [CrossRef]
13. [12] Bidaoui H, el Abbassi I, el Bouardi A, Darcherif A. Wind speed data analysis using Weibull and Rayleigh Distribution functions, case study: Five cities northern Morocco. Procedia Manuf 2019;32:786–793.
14. [13] Balpetek N, Kavak Akpinar E. Statistical analysis of Wind Speed Distribution with Sinop Turkey application. J Ther Eng 2019;5:277–292. [CrossRef]
15. [14] Serban A, Paraschiv LS, Paraschiv S. Assessment of wind energy potential based on Weibull and Rayleigh Distribution models. Energy Rep 2020;6:250–267. [CrossRef]
16. [15] Mert İ, Karakuş C. A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull Distributions in Antakya, Turkey. Turk J Electr Eng Comput Sci 2015;23:1571–1586. [CrossRef]
17. [16] Ozay C, Celiktas MS. Statistical analysis of wind speed using two-parameter Weibull Distribution in Alaçatı region. Energy Convers Manag 2016;121:49–54. [CrossRef]
18. [17] Chiodo E, de Falco P. Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation. Electr Power Syst Res 2016;141:549–561. [CrossRef]
19. [18] Akgül FG, Şenoğlu B, Arslan T. An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Convers Manag 2016;114:7358–7373. [CrossRef]
20. [19] Mohammadi K, Alavi O, McGowan JG. Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review. Energy Convers Manag 2017;143:109– 122. [CrossRef]
21. [20] Aries N, Boudia SM, Ounis H. Deep assessment of wind speed distribution models: A case study of four sites in Algeria. Energy Convers Manag 2018;155:78–90. [CrossRef]
22. [21] Dursun B, Alboyaci B. An evaluation of wind energy characteristics for four different locations in Balikesir. Energy Source Part A: Recover Util Environ Effect 2011;33:1086–1103. [CrossRef]
23. [22] Ucar A, Balo F. Evaluation of wind energy potential and electricity generation at six locations in Turkey. Appl Energy 2009;86:1864–1872. [CrossRef]
24. [23] Ozerdem B, Turkeli M. An investigation of wind characteristics on the campus of Izmir Institute of Technology, Turkey. Renew Energy 2003;28:1013–1027. [CrossRef]
25. [24] Ilkiliç C, Nursoy M. The potential of wind energy as an alternative source in Turkey. Energy Source Part A: Recover Util Environ Effect 2010;32:450–459. [CrossRef] [25] Arslan T, Acitas S, Senoglu B. Generalized Lindley and Power Lindley distributions for modeling the wind speed data. Energy Convers Manag 2017;152:300–311. [CrossRef]
26. [26] Hepbasli A, Ozdamar A, Ozalp N. Present status and potential of renewable energy sources in Turkey. Energy Source 2001;23:631–648. [CrossRef]
27. [27] Suzer AE, Atasoy VE, Ekici S. Developing a holistic simulation approach for parametric techno-economic analysis of wind energy. Available at: https://econpapers.repec.org/article/eeeenepol/v_3a149_3ay_3a2021_3ai_3ac_3as0301421520308168.htm. Accessed on May 13, 2024.
28. [28] Chang TP. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Appl Energy 2011;88:272–282. [CrossRef]
29. [29] Tizgui I, el Guezar F, Bouzahir H, Benaid B. Comparison of methods in estimating Weibull parameters for wind energy applications. Available at: https://www.emerald.com/insight/content/doi/10.1108/IJESM-06-2017-0002/full/html. Accessed on May 13, 2024.
30. [30] Kecskés I, Székács L, Fodor JC, Odry P. PSO and GA optimization methods comparison on simulation model of a real hexapod robot. In proceedings of the 9th International Conference on Computational Cybernetics. 2013; Tihany, Hungary. IEEE; 2013. [CrossRef]
31. [31] Goudarzi S, Hassan WH, Anisi MH, Soleymani SA. Comparison between hybridized algorithm of GA–SA and ABC, GA, DE and PSO for vertical-handover in heterogeneous wireless networks. Sadhana Acad Proc Eng Sci 2016;41:727–753. [CrossRef]
32. [32] Song M, Chen DM. A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem. Ann GIS 2018;24:19–31. [CrossRef]
33. [33] Özsoy VS, Ünsal MG, Örkcü HH. Use of the heuristic optimization in the parameter estimation of generalized gamma distribution: Comparison of GA, DE, PSO and SA methods. Comput Stat
34. 2020;35:1895–1925. [CrossRef]
35. [34] Storn R, Price K. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 1997;11:341–359. [CrossRef]
36. [35] Das S, Mullick SS, Suganthan PN. Recent advances in differential evolution-An updated survey. Swarm Evol Comput 2016;27:1–30. [CrossRef]
37. [36] Akın A, Sunar F, Berberoğlu S. Urban change analysis and future growth of Istanbul. Environ Monit Assess 2015;187:4721. [CrossRef]