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Validation of wind resource in 14 locations of Nepal

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  • Laudari, R.
  • Sapkota, B.
  • Banskota, K.

Abstract

In the highly traditional and inefficient energy dependent countries like Nepal effective exploitation of renewable energy need serious attention. In this context, identification of potential locations for wind energy production is the particular interest of Nepal. Wind speed is the most important indicator for assessing the wind energy resource. Wind resource assessment is carried out either by microscale modeling or dedicated masts or by means of both. Measuring wind energy potential by establishing masts demands high cost and longer time period. Hence it is important to validate the available modeled wind speed with the observed data. The modeled wind data produced by High Asia Refined Analysis dataset are validated based on the observed data of 14 locations in this research. Statistical analysis is computed and also wind speed hourly data of all study sites are compared by presenting both sources data graphically. The statistical analysis supports that the two sources of data do not differ significantly and there is moderate correlation between these data sources. The validation result shows that the modeled wind dataset represents moderately the actual wind speed situation of the studied locations. Thus this modeled dataset is useful for preliminary assessment of wind in Nepal.

Suggested Citation

  • Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:777-786
    DOI: 10.1016/j.renene.2017.10.068
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    References listed on IDEAS

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    1. Timilsina, Govinda R. & Cornelis van Kooten, G. & Narbel, Patrick A., 2013. "Global wind power development: Economics and policies," Energy Policy, Elsevier, vol. 61(C), pages 642-652.
    2. Nor, Khalid Mohamed & Shaaban, Mohamed & Abdul Rahman, Hasimah, 2014. "Feasibility assessment of wind energy resources in Malaysia based on NWP models," Renewable Energy, Elsevier, vol. 62(C), pages 147-154.
    3. Kim, Hyeonwu & Kim, Bumsuk, 2016. "Wind resource assessment and comparative economic analysis using AMOS data on a 30 MW wind farm at Yulchon district in Korea," Renewable Energy, Elsevier, vol. 85(C), pages 96-103.
    4. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    5. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
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