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Journals / Journal of Thermal Engineering / 2019 / Cilt: 5 - Sayı: 4

STATISTICAL ANALYSIS OF WIND SPEED DISTRI BUTION WITH SINOP TURKEY APPLICATION

Ebru KAVAK AKPINAR
N. BALPETEK
Pages
277–292
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The Weibull distribution function was found to be more appropriate than theRayleigh distribution functi on.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the wind energy potential of the Sinop region was analyzed statistically by using the TurkishState Meteorological Station’s hourly wind speed data between the years of 2005 2014.
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Key Findings & Data:The best way of representing the performance of the Weibulland Rayleigh distributions is to use the statistical parameters such as the correlation coefficient (R 2 ), chi square (χ 2 )and the root mean square error analysis (RMSE).
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Conclusion & Impact:The Weibull distribution function was found to be more appropriate than theRayleigh distribution functi on.
🔬Design:Research Article
📍Scope:2005 Verileri
⚙️Variables:Domain Parameters

Abstract

In this study, the wind energy potential of the Sinop region was analyzed statistically by using the TurkishState Meteorological Station’s hourly wind speed data between the years of 2005 2014. The two parameter Weibulland one parameter Rayleigh probabi lity distribution functions were used to determine the wind energy potential ofthe region. The probability distribution functions were derived from the cumulative function and used to calculate themean wind speed and the variance of the actual data. The best way of representing the performance of the Weibulland Rayleigh distributions is to use the statistical parameters such as the correlation coefficient (R 2 ), chi square (χ 2 )and the root mean square error analysis (RMSE). The results of the study show ed that Sinop has a mean wind speedof 3.36 m/s with a maximum value of 4.28 m/s in February of 2011, and a minimum value of 2.41 m/s in March of2013, while the corresponding mean wind power density is approximately 33.31 W/m 2 for the whole year. In gener al,it was determined the wind speed is higher during some winter and spring months, notably February and March, andis lower during the autumn months. The Weibull distribution function was found to be more appropriate than theRayleigh distribution functi on.

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Cite This Article

AKPINAR, E. K., BALPETEK, N. (2022). STATISTICAL ANALYSIS OF WIND SPEED DISTRI BUTION WITH SINOP TURKEY APPLICATION. Journal of Thermal Engineering, Cilt: 5 - Sayı: 4, 277–292. https://ajindex.com/articles/566782

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References (15)

  1. Pishgar Komleh, S.H., Keyhani, A., Sefeedpari, P. (2015). Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews, 42, 313 322
  2. Sohoni, V., Gupta, S., Nema, R. (2016). A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turk J Elec Eng & Comp Sci, 24, 4724 4735
  3. Morgan, V.T. (1995). Statistical distributions of wind paramete rs at Sydney, Australia. Renew Energy, 6, 39 47
  4. Seguro, J.V., Lambert, T.W. (2000). Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J Wind Eng Ind Aerod, 85, 75 84
  5. Costa Rocha, P.A., De Sousa, R.C., De Andrade, C.F., Da Silva, M.E.V. (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl Energ, 89, 395 400
  6. Ulgen, K., Hepbasli, A. (2002). Determin ation of Weibull parameters for wind energy analysis of Izmir, Turkey. Int J Energy Res, 26, 494 506
  7. Celik, A.N. (2003). 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
  8. Karsli, V.M., Gecit, C. (2003). An investigation on wind power potential of Nurdagı Gaziantep, Turkey. Renew Energy, 28, 823 830
  9. Kose, R., Ozgur, M. A., Erbas, O., Tugcu, A. (2004). The analysis of wind data and energy p otential in Kutahya, Turkey. Renew Sustain Energy Rev, 8, 277 288
  10. Akpinar, E.K., Akpinar, S. (2004). Determination of the wind energy potential for Maden Elazığ, Turkey. Energy Conversion and Management, 45, 2901 2914
  11. Akpinar, E.K., Akpinar, S. (2004). Statistical Analysis of wind energy potential on the basis of the Weibull and Rayleigh distribution for Ağın Elazığ, Turkey. J.Power Energy, 218, 557 565
  12. Genc, A., Erisoglu, M., Pekgor, A., Oturanc, G., Hepbasli, A., Ulgen, K. (2005). Est imation of wind power potential using Weibull distribution. Energ Source, 27, 809 822
  13. Akpinar, E.K. (2006). A statistical investigation of wind energy potential. Energy Sources, Part A, 28, 807 820
  14. Gökcek, M., Bayülken, A., Bekdemir, Ş. (2007) 2007). Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey. Renewable Energy, 32, 1739 1752
  15. Yilmaz, V., Çelik, H.E. ( 2008). A statistical approach to estimate the wind sp