Open Access
Sci. Tech. Energ. Transition
Volume 79, 2024
Article Number 10
Number of page(s) 15
Published online 20 February 2024
  • Ministry of New and Renewable Energy, Government of India (2024) Wind overview. Available at [Google Scholar]
  • National Institute of Wind Energy, Government of India (2024) Wind resource assessment - Numerical wind atlas files of India. Available at [Google Scholar]
  • Her C., Sambor D.J., Whitney E., Wies R. (2021) Novel wind resource assessment and demand flexibility analysis for community resilience: A remote microgrid case study, Renew. Energy 179, 1472–86. [CrossRef] [Google Scholar]
  • Dayal K.K., Cater J.E., Kingan M.J., Bellon G.D., Sharma R.N. (2021) Wind resource assessment and energy potential of selected locations in Fiji, Renew. Energy 172, 219–37. [CrossRef] [Google Scholar]
  • Neupane D., Kafle S., Karki K.R., Kim D.H., Pradhan P. (2022) Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis, Renew. Energy 181, 278–91. [CrossRef] [Google Scholar]
  • Franke K., Sensfuß F., Deac G., Kleinschmitt C., Ragwitz M. (2021) Factors affecting the calculation of wind power potentials: A case study of China, Renew. Sust. Energy Rev. 149, 1–13. [Google Scholar]
  • Vinhoza A., Schaeffer R. (2021) Brazil’s offshore wind energy potential assessment based on a spatial multi-criteria decision analysis, Renew. Sust. Energy Rev. 146, 1–14. [Google Scholar]
  • Ayik A., Ijumba N., Kabiri C., Goffin P. (2021) Preliminary wind resource assessment in South Sudan using reanalysis data and statistical methods, Renew. Sust. Energy Rev. 138, 1–22. [Google Scholar]
  • Kumar M.B.H., Balasubramaniyan S., Padmanaban S., Holm-Nielsen J.B. (2019) Wind energy potential assessment by Weibull parameter estimation using multiverse optimization method: a case study of Tirumala region in India, Energies 12, 1–21. [Google Scholar]
  • Chandel S.S., Ramasamy P., Murthy K.S.R. (2014) Wind power potential assessment of 12 locations in western Himalayan region of India, Renew. Sust. Energy Rev. 39, 530–45. [CrossRef] [Google Scholar]
  • Chandel S.S., Murthy K.S.R., Ramasamy P. (2014) Wind resource assessment for decentralized power generation: case study of a complex hilly terrain in western Himalayan region, Sustain. Energy Technol. Assess. 8, 18–33. [Google Scholar]
  • Sangroya D., Nayak J.K. (2015) Development of wind energy in India, Int. J. Renew. Energy Res. 5, 1, 1–13. [Google Scholar]
  • Ramachandra T.V., Hegde Ganesh, Krishnadas Gautham (2014) Potential assessment and decentralized applications of wind energy in Uttarakhand, Karnataka, Int. J. Renew. Energy Res. 4, 1, 1–10. [Google Scholar]
  • Hossain J., Sinha V., Kishore V.V.N. (2011) A GIS based assessment of potential for windfarms in India, Renew. Energy 36, 3257–3267. [CrossRef] [Google Scholar]
  • Singh R., Prakash O. (2018) Wind energy potential evaluation for power generation in selected districts of Jharkhand, Energy Sources A: Recovery Util. Environ. Eff. 40, 6, 673–679. [CrossRef] [Google Scholar]
  • Reddy G.K.K., Reddy S.V., Ramkumar T.K., Sarojamma B. (2014) Wind power density analysis for micro-scale wind turbines, Int. J. Eng. Sci. 3, 12, 53–60. [Google Scholar]
  • Murthy K.S.R., Rahi O.P. (2016) Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India. Renew. Energy 99, 1137–45. [CrossRef] [Google Scholar]
  • Stevens M.J.M., Smulders P.T. (1979) Estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes, Wind Eng. 3, 2, 132–45. [Google Scholar]
  • Manwel J.F., Mcgowan J.G., Rogers A.L. (2009) Wind energy explained theory, design and application, John Wiley & Sons Ltd., UK. [CrossRef] [Google Scholar]
  • Justus C.G., Hargraves W.R., Mikhail A., Graber D. (1978) Methods for estimating wind speed frequency distributions, J. Appl. Meteorol. 17, 3, 350–353. [CrossRef] [Google Scholar]
  • Chaurasiya P.K., Ahmed S., Warudkar V. (2018) Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument, Alex. Eng. 57, 4, 2299–2311. [CrossRef] [Google Scholar]
  • Tizgui I., Guezar F.E., Bouzahir H., Benaid B. (2017) Comparison of methods in estimating Weibull parameters for wind energy applications, Int. J. Energy Sec. Manag. 11, 4, 650–63. [CrossRef] [Google Scholar]
  • Zohbi G.A., Hendrick P., Bouillard P. (2014) Evaluation du potentiel d’énergie éolienne au Liban, Rev. Energ. Renouv. 17, 1, 83–96. [Google Scholar]
  • Tizgui I., Guezar F.E., Bouzahir H., Benaid B. (2017) Comparison of methods in estimating Weibull parameters for wind energy applications, Int. J. Energy Sec. Manag. 11, 4, 650–663. [CrossRef] [Google Scholar]
  • Mathew S. (2006) Wind energy fundamentals, resource analysis and economics, Springer, Berlin. [Google Scholar]
  • Akdağ S.A., Güler O. (2015) A novel energy pattern factor method for wind speed distribution parameter estimation, Energy Conver. Manag. 106, 1124–33. [CrossRef] [Google Scholar]
  • Mohammadi M., Alavi O., Mostafaeipour A., Goudarzi N., Jalilvand M. (2016) Assessing different parameters estimation methods of Weibull distribution to compute wind power density, Energy Conver. Manag. 108, 322–335. [CrossRef] [Google Scholar]
  • Ling J., Lublertlop K. (2015) Economic analysis of wind turbine installation in Taiwan, Math. Probl. Eng. 2015, 614514. [Google Scholar]
  • Fingersh L., Hand M., Laxson A. (2006) Wind turbine design cost and scaling model, Technical Report NREL/TP-500-40566, National Renewable Energy Laboratory, CO, USA. [Google Scholar]
  • Carrillo C., Obando Montaño A.F., Cidrás J., Díaz-Dorado E. (2013) Review of power curve modelling for wind turbines, Renew. Sust. Energy Rev. 21, 572–581. [CrossRef] [Google Scholar]
  • Irwanto M., Gomesh N., Mamat M.R., Yusoff Y.M. (2014) Assessment of wind power generation potential in Perlis, Malaysia. Renew. Sust. Energy Rev. 38, 296–308. [CrossRef] [Google Scholar]
  • NASA. Surface meteorology and solar energy release 6.0 methodology. Available at (accessed 1 June 2021). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.