Statistical analysis of wind energy potential for bartın province: Weibull distribution approach

Abstract

Our study aimed to investigate the wind potential of Bartın, one of Türkiye's northernmost provinces. To determine this potential, we analyzed the region's wind power density and wind speed parameters. 3561 hourly wind speed data obtained from the stations of the General Directorate of Meteorology between the years 2015-2024 were used in the analyses. The obtained data were evaluated to examine the annual, seasonal, and monthly distributions of wind speed. In the actual data collected, the highest wind speed was measured as 1.5645 m/s and the lowest as 0.3345 m/s. Based on the Weibull analysis, the highest average wind speed was determined to be 1.2712 m/s in 2024, and the highest power density corresponding to this average wind speed was 1.6453 W/m². The obtained data were evaluated in order to examine the annual, seasonal and monthly distributions of wind speed. Weibull distribution function, which is frequently preferred in the literature and known to provide reliable results, was used for statistical modeling of wind speeds. The shape (k) and scale (c) parameters of the Weibull distribution were determined by the least squares method; the suitability of the model was statistically verified by the coefficient of determination (R²), root mean square error (RMSE) and chi-square (χ²) tests. The analyses show that the wind data of Bartın province can be successfully represented by the Weibull distribution. The findings shed light on the feasibility of wind energy-based investments in Bartın and provide a solid data basis for new studies in this field. It is also anticipated that they may contribute to the planning of sustainable energy policies at the regional level.

Keywords

• Wind energy
• statistical wind analysis
• Weibull distribution
• Bartın

Bartın ili için rüzgâr enerjisi potansiyelinin istatistiksel analizi: Weibull dağılımı yaklaşımı

Öz

Bu çalışmada, Türkiye’nin en kuzeyinde yer alan Karadeniz Bölgesi’nin Bartın ilindeki rüzgâr enerjisi potansiyelinin belirlenmesine yönelik olarak, rüzgâr hızı ve güç yoğunluğu parametrelerinin istatistiksel analizi gerçekleştirilmiştir. Analizlerde 2015-2024 yılları arasında Meteoroloji Genel Müdürlüğü istasyonlarının saatlik olarak aldığı 3561 adet rüzgâr hızı verileri kullanılmıştır. Elde edilen veriler, rüzgâr hızının yıllık, mevsimlik ve aylık dağılımlarını incelemek amacıyla değerlendirilmiştir. Toplanan gerçek verilerde rüzgar hızı en yüksek 1.5645 m/s, en düşük 0.3345 m/s olarak ölçülmüştür. Weibull analizi sonucunda en yüksek ortalama rüzgar hızı 2024 yılında 1,2712 m/s, bu ortalama rüzgar hızına karşılık gelen en yüksek güç yoğunluğu ise 1,6453 W/m² olarak tespit edilmiştir. Rüzgâr hızlarının istatistiksel modellemesi için literatürde sıklıkla tercih edilen ve güvenilir sonuçlar sunduğu bilinen Weibull dağılım fonksiyonu kullanılmıştır. Weibull dağılımının şekil (k) ve ölçek (c) parametreleri, en küçük kareler yöntemiyle belirlenmiş; modelin uygunluğu ise belirleme katsayısı (R²), kök ortalama kare hata (RMSE) ve ki-kare (χ²) testleri ile istatistiksel olarak doğrulanmıştır. Yapılan analizler, Bartın ilinin rüzgâr verilerinin Weibull dağılımı ile başarılı biçimde temsil edilebildiğini göstermektedir. Elde edilen bulgular, Bartın’da rüzgâr enerjisi temelli yatırımların fizibilitesine ışık tutmakta olup, bu alanda yapılacak yeni çalışmalar için sağlam bir veri temeli sunmaktadır. Ayrıca, sürdürülebilir enerji politikalarının bölgesel düzeyde planlanmasında da katkı sağlayabileceği öngörülmektedir.

Anahtar Kelimeler

• Rüzgâr enerjisi
• istatistiksel rüzgâr analizi
• Weibull dağılımı
• Bartın

Kaynakça

1. [1]Willemsen E, Wisse J. A. “Design for wind comfort in The Netherlands: Procedures, criteria and open research issues. Journal of Wind Engineering and Industrial”. Aerodynamics, 95(9-11),1541-1550, 2007..
2. [2]Ikegaya N, Ikeda Y, Hagishima A, Tanimoto J. “Evaluation of rare velocity at a pedestrian level due to turbulence in a neutrally stable shear flow over simplified urban arrays”. Journal of Wind Engineering and Industrial Aerodynamics, 171,137-147, 2017.
3. [3]Wang W, Ikegaya N, Okaze T. “Comparing Weibull distribution method and Gram–Charlier series method within the context of estimating low-occurrence strong wind speed of idealized building cases”. Journal of Wind Engineering and Industrial Aerodynamics, 236, 105401, 2023.
4. [4]Blocken B. “50 years of computational wind engineering: past, present and future”. Journal of Wind Engineering and Industrial Aerodynamics, 129, 69-102, 2014.
5. [5]Blocken B, Stathopoulos T, Van Beec J. “Pedestrian-level wind conditions around buildings: Review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment”. Building and Environment, 100, 50-81, 2016.
6. [6]Takadate Y, Okuda Y. “Wind Tunnel Study on Wind Speeds near the Ground with Roughness Blocks”. Advanced Experimental Mechanics, 7, 162-167, 2022.
7. [7]Takemi T, Yoshida T, Horiguchi M, Vanderbauwhede W. (2020). “Large-Eddy-simulation analysis of airflows and strong wind hazards in urban areas”. Urban Climate, 32, 100625.
8. [8]Efthimiou G C, Hertwig D, Andronopoulos S, Bartzis J G, Coceal O. “A statistical model for the prediction of wind-speed probabilities in the atmospheric surface layer. Boundary-Layer Meteorology, 163, 179-201, 2017.

9. [9]Efthimiou GC, Kuma P, Giannissi, SG, Feiz AA, Andronopoulos S. “Prediction of the wind speed probabilities in the atmospheric surface layer”. Renewable energy, 132, 921-930, 2019.
10. [10]Ikegaya N, Kawaminami T, Okaze T, Hagishima A. “Evaluation of exceeding wind speed at a pedestrian level around a 1: 1: 2 isolated block model”. Journal of Wind Engineering and Industrial Aerodynamics, 201, 104193, 2020.
11. [11]Demirkol Z, Dayi F., Erdoğdu A, Yanik A, Benek A. “A Techno-Economic Analysis of Power Generation in Wind Power Plants Through Deep Learning: A Case Study of Türkiye”. Energies, 18(10), 2632, 2025.
12. [12]Wang W, Gao Y, Ikegaya N. “Approximating wind speed probability distributions around a building by mixture weibull distribution with the methods of moments and L-moments”. Journal of Wind Engineering and Industrial Aerodynamics, 257, 106001, 2025.
13. [13]Han F, Li X, Qi S, Wang W, Shi W. “Reliability analysis of wind turbine subassemblies based on the 3-P Weibull model via an ergodic artificial bee colony algorithm”. Probabilistic Engineering Mechanics, 73, 103476, 2023.
14. [14]Huo X, Yang L, Li D. H. “Determining Weibull distribution patterns for wind conditions in building energy-efficient design across the different thermal design zones in China”. Energy, 304, 132013, 2024.
15. [15]Basumatary H, Sreevalsan E, Sasi KK. “Weibull parameter estimation—A comparison of different methods”. Wind Engineering, 29(3), 309-315, 2005.
16. [16]Ukoima KN, Okoro OI, Akuru UB, Davidson IE. “Determination of the Weibull parameters and wind power potential: A case of Okorobo-Ile town, Rivers state, Nigeria”. Wind Energy and Engineering Research, 2, 100006, 2024.
17. [17]El Kihel B, Elyamani NK, Chillali A. “Wind energy potential assessment using the Weibull distribution method for future energy self-sufficiency”. Scientific African, 26, e02482, 2024.
18. [18]Wang L, Liu R, Zeng W, Zhang L, Peng H, Calautit JK, Li X. “Revealing the theoretical wind potential of the Qinghai-Tibet Plateau: A novel Bayesian Monte-Carlo framework for the Weibull bivariate distribution”. Energy Conversion and Management, 325, 119375, 2025.
19. [19]Rüstemli S, Güntas O, Şahin G, Koç A, Van Sark W, Doğan SŞ. “Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey”. Energy Strategy Reviews, 56, 101552, 2024.
20. [20]Can ÖF. “Numerical investigation of wind resistance and heat island formation in buildings of different configurations”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 30(6), 729-736, 2024.
21. [21] Celik AN. “A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey”. Renewable Energy, 29(4), 593-604, 2004.
22. [22] Akdağ SA, Dinler A. “A new method to estimate Weibull parameters for wind energy applications”. Energy Conversion and Management, 50(7), 1761–1766, 2009.
23. [23]Seguro JV, Lambert TW. “Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis”. Journal of Wind Engineering and Industrial Aerodynamics, 85(1), 75-84, 2004.
24. [24]T.C. Meteoroloji Genel Müdürlüğü (MGM). “Bartın Meteorolojik Gözlem Verileri (2015–2024)‘’ https://www.mgm.gov.tr/sondurum/guncel-haritalar.aspx (04.05.2025)
25. [25]Bilgili M, Yasar A, Simsek E. “Offshore wind power development in Europe and its comparison with onshore counterpart”. Renewable and Sustainable Energy Reviews, 15(2),905-915, 2011.
26. [26]Gungor A, Gokcek M, Uçar H, Arabacı E, Akyüz A. “Analysis of wind energy potential and Weibull parameter estimation methods: a case study from Turkey”. International Journal of Environmental Science and Technology, 17(2), 1011-1020, 2020.
27. [27] Akpınar EK, Balpetek N. “Statistical analysis of wind energy potential of Elazığ province according to Weibull and Rayleigh distributions”. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(1), 569-580, 2019.
28. [28]T.C. Meteoroloji Genel Müdürlüğü (MGM). “Otomatik Meteoroloji Gözlem İstasyonları (OMGİ) Veritabanı: 2015–2024 Yılları Arası Rüzgâr Verileri’’ https://www.mgm.gov.tr/sondurum/radar.aspx (04.05.2025)
29. [29]Gong Z, Fang P, Wang Z, Li X, Wang Z, Meng F. “Pyrolysis characteristics and products distribution of haematococcus pluvialis microalgae and its extraction residue”. Renewable Energy, 146, 2134-2141, 2020.
30. [30]Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for Estimating Wind Speed Frequency Distributions. Journal of Applied Meteorology, 17(3), 350–353. https://doi.org/10.1175/1520-0450(1978)017<0350:mfewsf>2.0.co;2
31. [31] Seguro, J. V., & Lambert, T. W. (2000). Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics, 85(1), 75–84. https://doi.org/10.1016/s0167-6105(99)00122-1
32. [32]YILMAZ, A., KARA, M., & AYDOĞDU, H. (2020). A study on comparisons of Bayesian and classical parameter estimation methods for the two-parameter Weibull distribution. Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 576–602. https://doi.org/10.31801/cfsuasmas.606890
33. [33]Yılmaz, E., Kara, D., & Aydoğdu, N. (2020). A study on comparisons of Bayesian and classical parameter estimation methods for the two-parameter Weibull distribution. Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics, 69(2), 848–865. https://doi.org/10.31801/cfsuasmas.606890
34. [34]Erişoğlu, M., Aras, S., & Yıldızay, H. (2020). Optimum method for determining Weibull distribution parameters used in wind energy estimation. Pakistan Journal of Statistics and Operation Research, 16(2), 345–356. https://pjsor.com/pjsor/article/view/3456
35. [35] Celik, A. N. (2003). Assessing the suitability of wind speed probabilty distribution functions based on wind power density. Renewable Energy, 28(10), 1563-1574.