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Wind Power Density Estimation using Meteorological Tower Data

Received: 3 April 2013    Accepted:     Published: 30 May 2013
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Abstract

The amount of power in the wind is very dependent on the speed of the wind. Because the power in the wind is proportional to the cube of the wind speed, small differences in the wind speed make a big difference in the power you can make from it. A 10% difference in speed makes about a 33% change in power. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. In this paper the important aspects of wind resource assessment for a period of three years from 2010-2012 will be studied for a 50 meter instrumented meteorological tower located at Sathyabama University, Chennai.

Published in International Journal of Renewable and Sustainable Energy (Volume 2, Issue 3)
DOI 10.11648/j.ijrse.20130203.15
Page(s) 110-114
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wind Resource Assessment, Wind Speed, Wind Energy, Meteorological Tower Data

References
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[2] T.V.Ramachandran, et. al. "Wind Energy Potential Assessment Spatial Decision Support System", Energy Education Science & Technology 2005 Volume 14 Issue 2, pp 61-80.
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[4] L. Landberg, "Short Term Prediction of the Power Production from Wind Farms",Journal ofWind Engineering and Industrial Aerodynamics, Vol. 80, 1999, pp.207-220.
[5] Sancho Salcedo-Sanz, et. al., "Accurate Short-term Wind Speed Prediction by Exploiting Diversity in Input Data using Banks of rtificial Neural Networks", Journal of Neurocomputing, Vol 72, 2009, pp.1336-1341.
[6] M. Carolin Mabel & E.Fernandez, " Analysis of wind power generation and prediction using ANN – A Case study" Renewale Energy, 33(2008), pp.986-992.
[7] K. Sreelakshmi, P.Ramakanthkumar, " Neural Network for short term wind speed prediction", World Acadam of Science & Engineering technology, 42(2008), pp. 221-225.
[8] Hafzullah Aksoy, Z. Fuat Toprak, Ali Aytek, N.Erdem Unal, " Stochastic generation of hourly mean wind speed data", Renewable Energy 29(2004), pp.2111-2131.
[9] "Wind Energy- windpower.org" Mohammad Monfared, Hasan Rastegar, Hossein Madadi Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods", Renewable Energy, 34(2009), pp.845-848.
[10] Andrew Kusiak, Haiyang Zheng, Zhe Song, " Models for monitoring wind farm power", Renewable Energy, 34(2009), pp.583-590.
[11] S.Kumar, "Wind Energy – India Overview" Renewable Energy, 16(1999) pp.961-964
[12] Sancho Salcedo-Sanz, et. al., "Hybridizing the fifth Generation Mesoscale Model with Artificial Neural Networks for Short-term Wind Speed Prediction", Journal of Renewable Energy, Vol. 34, 2009, 1451-1457.
[13] Mehmet Bilgili, Besir Sahin, Abdulkadir Yasar, " Application of Artificial Neural Networks for the Wind Speed Prediction of Target Station using Reference Stations Data", Journal of Renewable Energy, Vol. 32, 2007, 2350-2360.
[14] M.A. Mohandes, et. al., " Support Vector Machines for Wind Speed Prediction", Journal of Renewable Energy, Vol. 29, 2004, Pp 939-947.
[15] Uffe B.Kiaerulff , Anders L.Madsen "Probabilistic Network- An Introduction to Bayesian Networks and Influence Diagrams", Technical Report, May 2005, Aalborg University.
[16] G.H.Bakir, et, al., "Predicting Structured Data", Cambridge: MIT Press, 2007.
[17] Jeasen,F.V.,"An Introduction to Bayesian Networks", UCL Press , London, 1996.
[18] L.C.Van der Gaag and P.R.de Wall, "Multi-Dimensional Bayesian Network Classifiers",Third Eruopean Conference on Probabilistic Graphical Models. Pp 107-114,2006sardarmaran@gmail.com(Sardar Maran P), rponnusamy@acm.org(Ponnusamy R)
[19] S.Kumar, "Wind Energy – India Overview" Renewable Energy, 16(1999) pp.961-964
[20] Sancho Salcedo-Sanz, et. al., "Hybridizing the fifth Generation Mesoscale Model with Artificial Neural Networks for Short-term Wind Speed Prediction", Journal of Renewable Energy, Vol. 34, 2009, 1451-1457.
[21] Mehmet Bilgili, Besir Sahin, Abdulkadir Yasar, " Application of
[22] Artificial Neural Networks for the Wind Speed Prediction of Target Station using Reference Stations Data", Journal of Renewable Energy, Vol. 32, 2007, 2350-2360.
[23] M.A. Mohandes, et. al., " Support Vector Machines for Wind Speed
[24] Prediction", Journal of Renewable Energy, Vol. 29, 2004, Pp 939-947.
[25] Uffe B.Kiaerulff , Anders L.Madsen "Probabilistic Network- An Introduction to Bayesian Networks and Influence Diagrams", Technical Report, May 2005, Aalborg University.
[26] G.H.Bakir, et, al., "Predicting Structured Data", Cambridge: MIT Press, 2007.
[27] Jeasen,F.V.,"An Introduction to Bayesian Networks", UCL Press , London, 1996.
[28] L.C.Van der Gaag and P.R.de Wall, "Multi-Dimensional Bayesian Network Classifiers",Third Eruopean Conference on Probabilistic Graphical Models. Pp 107-114,2006
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  • APA Style

    Sardar Maran P, Ponnusamy R. (2013). Wind Power Density Estimation using Meteorological Tower Data. International Journal of Sustainable and Green Energy, 2(3), 110-114. https://doi.org/10.11648/j.ijrse.20130203.15

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    ACS Style

    Sardar Maran P; Ponnusamy R. Wind Power Density Estimation using Meteorological Tower Data. Int. J. Sustain. Green Energy 2013, 2(3), 110-114. doi: 10.11648/j.ijrse.20130203.15

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    AMA Style

    Sardar Maran P, Ponnusamy R. Wind Power Density Estimation using Meteorological Tower Data. Int J Sustain Green Energy. 2013;2(3):110-114. doi: 10.11648/j.ijrse.20130203.15

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  • @article{10.11648/j.ijrse.20130203.15,
      author = {Sardar Maran P and Ponnusamy R},
      title = {Wind Power Density Estimation using Meteorological Tower Data},
      journal = {International Journal of Sustainable and Green Energy},
      volume = {2},
      number = {3},
      pages = {110-114},
      doi = {10.11648/j.ijrse.20130203.15},
      url = {https://doi.org/10.11648/j.ijrse.20130203.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijrse.20130203.15},
      abstract = {The amount of power in the wind is very dependent on the speed of the wind. Because the power in the wind is proportional to the cube of the wind speed, small differences in the wind speed make a big difference in the power you can make from it. A 10% difference in speed makes about a 33% change in power. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. In this paper the important aspects of wind resource assessment for a period of three years from 2010-2012 will be studied for a 50 meter instrumented meteorological tower located at Sathyabama University, Chennai.},
     year = {2013}
    }
    

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  • TY  - JOUR
    T1  - Wind Power Density Estimation using Meteorological Tower Data
    AU  - Sardar Maran P
    AU  - Ponnusamy R
    Y1  - 2013/05/30
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    DO  - 10.11648/j.ijrse.20130203.15
    T2  - International Journal of Sustainable and Green Energy
    JF  - International Journal of Sustainable and Green Energy
    JO  - International Journal of Sustainable and Green Energy
    SP  - 110
    EP  - 114
    PB  - Science Publishing Group
    SN  - 2575-1549
    UR  - https://doi.org/10.11648/j.ijrse.20130203.15
    AB  - The amount of power in the wind is very dependent on the speed of the wind. Because the power in the wind is proportional to the cube of the wind speed, small differences in the wind speed make a big difference in the power you can make from it. A 10% difference in speed makes about a 33% change in power. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. This gives rise to the primary reason for wind resource assessment. In order to more accurately predict the potential benefits of a wind power installation, wind speeds and other characteristics of a site’s wind regime must be accurately understood. In this paper the important aspects of wind resource assessment for a period of three years from 2010-2012 will be studied for a 50 meter instrumented meteorological tower located at Sathyabama University, Chennai.
    VL  - 2
    IS  - 3
    ER  - 

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Author Information
  • Centre for Earth & Atmospheric Sciences, Sathyabama University,Jeppiaar Nagar, Rajiv Gandhi Road,Chennai., Tamil Nadu, India

  • Madha Engineering College, Madha Nagar, Somangalam Road, Kundrathur, Chennai., Tamil Nadu, India

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