IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v119y2018icp777-786.html
   My bibliography  Save this article

Validation of wind resource in 14 locations of Nepal

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117310303
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2017.10.068?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.
    2. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    3. Chen, Hao & Gao, Xin-Ya & Liu, Jian-Yu & Zhang, Qian & Yu, Shiwei & Kang, Jia-Ning & Yan, Rui & Wei, Yi-Ming, 2020. "The grid parity analysis of onshore wind power in China: A system cost perspective," Renewable Energy, Elsevier, vol. 148(C), pages 22-30.
    4. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    5. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    6. Amani, Madjid & Ghenaiet, Adel, 2020. "Novel hybridization of solar central receiver system with combined cycle power plant," Energy, Elsevier, vol. 201(C).
    7. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    8. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    9. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Bai, Xinyu & Acord, Brendan & Wang, Peng, 2021. "Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    10. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    11. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    12. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    13. Jenniches, Simon & Worrell, Ernst & Fumagalli, Elena, 2019. "Regional economic and environmental impacts of wind power developments: A case study of a German region," Energy Policy, Elsevier, vol. 132(C), pages 499-514.
    14. Mohammed H. Alsharif & Jeong Kim & Jin Hong Kim, 2018. "Opportunities and Challenges of Solar and Wind Energy in South Korea: A Review," Sustainability, MDPI, vol. 10(6), pages 1-23, June.
    15. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Timilsina, Govinda R., 2021. "Are renewable energy technologies cost competitive for electricity generation?," Renewable Energy, Elsevier, vol. 180(C), pages 658-672.
    17. Victor Hugo Wentz & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, MDPI, vol. 15(7), pages 1-23, March.
    18. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    19. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    20. van Kooten, G. Cornelis & Withey, Patrick & Duan, Jon, 2020. "How big a battery?," Renewable Energy, Elsevier, vol. 146(C), pages 196-204.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:119:y:2018:i:c:p:777-786. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.