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Roughness classification utilizing remote sensing techniques for wind resource assessment

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  • Nayyar, Zeeshan Alam
  • Ali, Ahmed

Abstract

The rapid population growth and ever growing energy demand of Pakistan lead to economical downfall especially due to increase in its dependency on the import of petroleum products. Hence, the inclusion of alternate energy option is inevitable to the Country’s energy mix as the current conventional energy sources i.e. fossil fuels, nuclear and mega-hydro are unable to meet this growing demand. To develop the wind energy based facilities, wind resource assessment is a must, and it is also depends on number of meteorological, geographical, geomorphological and other relevant variables. Terrain roughness is one of these variables and is essential wind resource assessment. Previously, terrain roughness is computed through the field surveys or by using field wind speed data. The current research study is conducted and emphasize the advancement in wind resource assessment through the utilization of modern computing aids like satellite remote sensing analyses (RSA) especially for terrain roughness classification. In this paper, a novel method for terrain roughness classification is developed by utilizing RSA techniques over satellite imageries. Since this roughness classification technique only depends on RSA, it is served as an alternate to conventional ground surveying methods and therefore is time-efficient & cost-effective.

Suggested Citation

  • Nayyar, Zeeshan Alam & Ali, Ahmed, 2020. "Roughness classification utilizing remote sensing techniques for wind resource assessment," Renewable Energy, Elsevier, vol. 149(C), pages 66-79.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:66-79
    DOI: 10.1016/j.renene.2019.12.044
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    References listed on IDEAS

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    1. Nayyar, Zeeshan Alam & Zaigham, Nayyer Alam & Qadeer, Abdul, 2014. "Assessment of present conventional and non-conventional energy scenario of Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 543-553.
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    Cited by:

    1. Jung, Christopher & Schindler, Dirk, 2021. "A global wind farm potential index to increase energy yields and accessibility," Energy, Elsevier, vol. 231(C).
    2. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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