•  
  •  
 

Abstract

Reliable assessment of wind energy potential in regions characterized by complex terrain is often constrained by the limited availability of ground-based meteorological measurements. This study proposes an improved regression-based approach for refining wind speed estimates at the wind turbine hub height of 65 m using satellite-derived data from the NASA POWER database combined with a logarithmic vertical wind profile. The proposed methodology is validated using real operational data from a 750 kW wind power plant located in the mountainous Bostanlyk district of Uzbekistan for the period 2018–2021. The regression analysis demonstrates a strong linear relationship between the extrapolated wind speed and the actual annual electricity generation, yielding a coefficient of determination of R² = 0,886 and a Pearson correlation coefficient of r = 0,941, with a relative root mean square error below 5%. The surface roughness parameter z₀ = 0,5 is identified as the most representative value for the studied geomorphological conditions, providing the highest agreement between modeled wind speeds and measured energy output. The obtained results confirm the robustness and practical applicability of the proposed model for preliminary wind resource assessment in mountainous regions with a deficit of direct wind measurements. The developed approach can be effectively applied at the early design stage of wind energy projects to improve the reliability of wind potential estimation in similar complex-terrain environments.

First Page

69

Last Page

73

References

  1. European Wind Atlas (Troen & Petersen, 1989). https://www.wasp.dk/wind-atlases/european-wind atlas  
  2. Stackhouse, P. W., et al. (2021). NASA POWER Project: Langley Research Center. https://power.larc.nasa.gov
  3. S.Q. Shoguchkarov, T.R. Jamolov, N.Ye. Mukhtarov, I.A. Yuldoshev. Study of the Operating Parameters of a Wind Power Plant with 0.75 MW Power under the Conditions of Tashkent Oblast // Applied Solar Energy, 2018, Vol. 54, No. 5, pp. 392–394.
  4. Arian Bahrami, Amir Teimourian, Chiemeka Onyeka Okoye, Hamidreza Shiri. Technical and economic analysis of wind energy potential in Uzbekistan, Journal of Cleaner Production, Volume 223, 2019, pp. 801–814. https://doi.org/10.1016/j.jclepro.2019.03.140 
  5. I.A. Yuldoshev, T.R. Jamolov, Sh. Rustamova, B.M. Botirov. Assessment of energy performance of a 750 kW wind power plant // II International Scientific and Practical Conference “Problems and prospects of innovative Machinery and technologies in the Agri-Food Chain”, Tashkent, TSTU, April 22-23, 2022. (In Rus)
  6. I. Schicker, Johanna Ganglbauer, Markus Dabernig, T. Nacht. Wind power estimation on local scale. A case study of representativeness of reanalysis data and data-driven analysis. Frontiers in Climate, 2023. https://doi.org/10.3389/fclim.2023.1017774 
  7. Mohamed M.A., Wood D.H. Vertical Wind Speed Extrapolation Using the k−ε Turbulence Model // Wind Engineering, Volume 37, No. 1, 2013, pp. 13-36. https://doi.org/10.1260/0309-524X.37.1.13 
  8. Marvuglia, A., Messineo, A. Learning a Wind Farm Power Curve with a Data-Driven Approach, 2011, pp. 4217–4224. https://doi.org/10.3384/ECP110574217 
  9. Yuldoshev Isroil Abrievich, Jamolov Tulkin Rustamovich. Assessment of potential and techno-economic efficiency of small-scale wind energy in Tashkent region, Uzbekistan // Alternative Energy, No. 01 (16), 2025, pp. 105–140. (In Rus)
  10. Manwell, McGowan, Rogers. Wind Energy Explained: Theory, Design and Application.
  11. P. Ekanayake, Amila T. Peiris, J. Jayasinghe, Upaka S. Rathnayake. Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka. Mathematical Problems in Engineering, 2021, pp. 1–13. https://doi.org/10.1155/2021/4893713 
  12. Carta, J. A., et al. A review of wind speed probability distributions used in wind energy analysis, Renewable and Sustainable Energy Reviews, 13(5), 2009, pp. 933–955.
  13. P. Deepak, P. Jagadeesh. Performance Analysis of Novel Linear Regression Algorithm with Improved Accuracy Compared over K-Nearest Neighbor in Predicting Wind Power Generation. E3S Web of Conferences, 2024. https://doi.org/10.1051/e3sconf/202449102043 
  14. Manwell, J.F., McGowan, J.G., Rogers, A.L. Wind Energy Explained: Theory, Design and Application, 2nd ed., Wiley, 2010.
  15. Ignatyev Stanislav Georgievich, Kiseleva Sofya Valentinovna. Development of Methods for Assessing Wind Energy Potential and Calculating Annual Performance of Wind Turbines // Alternative Energy, 2010, No. 10. (In Rus)
  16. Petko Viktor Gavrilovich, Rakhimzhanova Ilmira Agzamovna, Shakhov Vladimir Alexandrovich. Approximation of the dependence of the wind energy utilization factor on the high-speed characteristics of agricultural wind turbines // Izvestiya OGAU, 2016, No. 6 (62). https://cyberleninka.ru/article/n/approksimatsiya-zavisimosti-koeffitsienta-ispolzovaniya-energii-vetra-ot-bystrohodnosti-vetroturbiny-vetroagregatov  (In Rus) 

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.