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Evaluation of a semi-empirical model for predicting the wind energy resource relevant to small-scale wind turbines

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  • Weekes, S.M.
  • Tomlin, A.S.

Abstract

An existing semi-empirical model for estimating the wind energy resource relevant to small-scale wind turbines has been investigated by comparing its predictions to wind speed data collected from 38 UK sites located in a variety of terrains. A range of error metrics have been used to judge the success of the model in predicting the mean wind speed and wind power density in each terrain type over five years. Averaged across all sites, the mean absolute and percentage errors were found to be 0.63 ms−1 and 18% with respect to the predicted mean wind speed and 23 wm−2 and 70% with respect to the predicted wind power density. The effect of tightening the definition of the canopy height, increasing the size of the fetch and incorporating directionally dependent regional roughness parameters, on the accuracy of the predictions was also investigated. It was found that by incorporating these factors into a modified model, the mean absolute and percentage errors could be reduced to 0.52 ms−1 and 16% with respect to the predicted mean wind speed. With the addition of an optimised Weibull shape factor, the average errors in the predicted wind power density were reduced to 20 wm−2 and 63%. The results indicate that while simple modifications can improve accuracy, these models should be applied with a degree of caution when attempting to make predictions of the viability of a proposed installation. Ideally, such models should be supplemented by other approaches in order to increase the confidence in the predicted wind resource.

Suggested Citation

  • Weekes, S.M. & Tomlin, A.S., 2013. "Evaluation of a semi-empirical model for predicting the wind energy resource relevant to small-scale wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 280-288.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:280-288
    DOI: 10.1016/j.renene.2012.06.053
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    Citations

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    Cited by:

    1. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    2. Osvaldo Rodriguez-Hernandez & Manuel Martinez & Carlos Lopez-Villalobos & Hector Garcia & Rafael Campos-Amezcua, 2019. "Techno-Economic Feasibility Study of Small Wind Turbines in the Valley of Mexico Metropolitan Area," Energies, MDPI, vol. 12(5), pages 1-26, March.
    3. Allen, D.J. & Tomlin, A.S. & Bale, C.S.E. & Skea, A. & Vosper, S. & Gallani, M.L., 2017. "A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data," Applied Energy, Elsevier, vol. 208(C), pages 1246-1257.
    4. Yossri, W. & Ben Ayed, S. & Abdelkefi, A., 2023. "Evaluation of the efficiency of bioinspired blade designs for low-speed small-scale wind turbines with the presence of inflow turbulence effects," Energy, Elsevier, vol. 273(C).
    5. Millward-Hopkins, J.T. & Tomlin, A.S. & Ma, L. & Ingham, D.B. & Pourkashanian, M., 2013. "Assessing the potential of urban wind energy in a major UK city using an analytical model," Renewable Energy, Elsevier, vol. 60(C), pages 701-710.
    6. Weekes, S.M. & Tomlin, A.S., 2014. "Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy," Renewable Energy, Elsevier, vol. 63(C), pages 162-171.
    7. Simões, Teresa & Estanqueiro, Ana, 2016. "A new methodology for urban wind resource assessment," Renewable Energy, Elsevier, vol. 89(C), pages 598-605.
    8. Millward-Hopkins, J.T. & Tomlin, A.S. & Ma, L. & Ingham, D.B. & Pourkashanian, M., 2013. "Mapping the wind resource over UK cities," Renewable Energy, Elsevier, vol. 55(C), pages 202-211.
    9. Weekes, S.M. & Tomlin, A.S., 2014. "Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP," Renewable Energy, Elsevier, vol. 68(C), pages 529-539.
    10. Luca Salvadori & Annalisa Di Bernardino & Giorgio Querzoli & Simone Ferrari, 2021. "A Novel Automatic Method for the Urban Canyon Parametrization Needed by Turbulence Numerical Simulations for Wind Energy Potential Assessment," Energies, MDPI, vol. 14(16), pages 1-22, August.
    11. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    12. Grieser, Benno & Sunak, Yasin & Madlener, Reinhard, 2015. "Economics of small wind turbines in urban settings: An empirical investigation for Germany," Renewable Energy, Elsevier, vol. 78(C), pages 334-350.
    13. Bush, Ruth & Jacques, David A. & Scott, Kate & Barrett, John, 2014. "The carbon payback of micro-generation: An integrated hybrid input–output approach," Applied Energy, Elsevier, vol. 119(C), pages 85-98.

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