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Dergiler / International Journal of Energy Applications and Technologies / 2017 / Cilt: 4 - Sayı: 3

Using a New Method based on Finsler Geometry for Wind Speed Modelling

Emrah DOKUR
Salim CEYHAN
Mehmet KURBAN
Sayfa
109–116
Yayın Tarihi
2017-10-25
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The performances of the models are given comparatively by using root mean square error (RMSE).

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Accurately modelling of wind speed is very important for the assessment of wind energy potential of a certain region.
📊
Temel Bulgular & Veri:For this reason, different distribution functions such as two-parameter Weibull, Gamma, Lognormal, Rayleigh etc. are proposed for accurately modeling wind speed in the literature.
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Sonuç & Katkı:The performances of the models are given comparatively by using root mean square error (RMSE).
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2015 Verileri
⚙️Değişkenler:Finsler Geometry · Wind Speed · Modelling

Abstract

Accurately modelling of wind speed is very important for the assessment of wind energy potential of a certain region. Before the installation of a wind energy conversion system in a region, the wind speed potential of that region needs to be determined and modelled. For this reason, different distribution functions such as two-parameter Weibull, Gamma, Lognormal, Rayleigh etc. are proposed for accurately modeling wind speed in the literature. In this paper, new probability and cumulative probability density functions based on Finsler geometry are proposed for wind speed modelling. Two-dimensional Finsler space metric function is obtained for Weibull distribution. Monthly analysis for Yalova, Turkey is realized using a new method based on Finsler geometry and two-parameter Weibull distribution. Wind data, consisting of hourly wind speed records between October 2015-September 2016 were obtained from the Yalova station of Turkish State Meteorological Service. The performances of the models are given comparatively by using root mean square error (RMSE).

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Bu Makaleye Atıf Yap

DOKUR, E., CEYHAN, S., KURBAN, M. (2017). Using a New Method based on Finsler Geometry for Wind Speed Modelling. International Journal of Energy Applications and Technologies, Cilt: 4 - Sayı: 3, 109–116. https://ajindex.com/articles/832775

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Kaynakça (16)

  1. Saleh H, Aly AA, Abdel-Hady S., "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt", Energy. 2012 Aug 31;44(1):710-9
  2. S. M. Metev and V. P. Veiko, Laser Assisted Microtechnology, 2nd ed., R. M. Osgood, Jr., Ed. Berlin, Germany: Springer-Verlag, 1998
  3. Garcia A, Torres JL, Prieto E, De Francisco A., "Fitting wind speed distributions: a case study.", Solar energy. 1998 Feb 28;62(2):139-44
  4. Luna RE, Church HW., "Estimation of long-term concentrations using a “universal” wind speed distribution. Journal of Applied Meteorology. 1974 Dec;13(8):910-6
  5. Justus CG, Hargraves WR, Yalcin A., "Nationwide assessment of potential output from wind-powered generators", Journal of applied meteorology. 1976 Jul;15(7):673-8
  6. Kiss P, Jánosi IM., "Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe", Energy Conversion and Management. 2008 Aug 31;49(8):2142-51
  7. Bardsley WE. Note on the use of the inverse Gaussian distribution for wind energy applications. Journal of Applied Meteorology. 1980 Sep;19(9):1126-30
  8. Vogel RM, McMahon TA, Chiew FH., "Floodflow frequency model selection in Australia", Journal of Hydrology. 1993 Jun 1;146:421-49
  9. Guttman NB, Hosking JR, Wallis JR., "Regional precipitation quantile values for the continental United States computed from L-moments. Journal of Climate", 1993 Dec;6(12):2326-40
  10. Stedinger JR., "Fitting log normal distributions to hydrologic data", Water Resources Research. 1980 Jun 1;16(3):481-90
  11. Kaminsky FC. "Four probability densities/log-normal, gamma, Weibull, and Rayleigh/and their application to modelling average hourly wind speed", International Solar Energy Society, Annual Meeting 1977 (pp. 19-6)
  12. Sherlock RH., "Analyzing winds for frequency and duration" InOn Atmospheric Pollution 1951 (pp. 42-49). American Meteorological Society
  13. Morgan EC, Lackner M, Vogel RM, Baise LG., "Probability distributions for offshore wind speeds", Energy Conversion and Management. 2011 Jan 31;52(1):15-26
  14. Takle ES, Brown JM., "Note on the use of Weibull statistics to characterize wind-speed data", Journal of applied meteorology. 1978 Apr;17(4):556-9
  15. Jaramillo OA, Borja MA., "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case", Renewable Energy. 2004 Aug 31;29(10):1613-30
  16. Rosen K, Van Buskirk R, Garbesi K. "Wind energy potential of coa