IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v29y2004i6p939-947.html
   My bibliography  Save this article

Support vector machines for wind speed prediction

Author

Listed:
  • Mohandes, M.A.
  • Halawani, T.O.
  • Rehman, S.
  • Hussain, Ahmed A.

Abstract

This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks. Mean daily wind speed data from Madina city, Saudi Arabia, is used for building and testing both models. Results indicate that SVM compare favorably with the MLP model based on the root mean square errors between the actual and the predicted data. These results are confirmed for a system with order 1 to system with order 11.

Suggested Citation

  • Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    • Handle: RePEc:eee:renene:v:29:y:2004:i:6:p:939-947
    DOI: 10.1016/j.renene.2003.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148103003860
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2003.11.009?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. Mohandes, Mohamed A. & Rehman, Shafiqur & Halawani, Talal O., 1998. "A neural networks approach for wind speed prediction," Renewable Energy, Elsevier, vol. 13(3), pages 345-354.
    2. Rehman, Shafiqur & Halawani, Talal Omar, 1994. "Statistical characteristics of wind in Saudi Arabia," Renewable Energy, Elsevier, vol. 4(8), pages 949-956.
    3. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    Full references (including those not matched with items on IDEAS)

    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. Rehman, S & Halawani, T.O & Mohandes, M, 2003. "Wind power cost assessment at twenty locations in the kingdom of Saudi Arabia," Renewable Energy, Elsevier, vol. 28(4), pages 573-583.
    2. Tasadduq, Imran & Rehman, Shafiqur & Bubshait, Khaled, 2002. "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renewable Energy, Elsevier, vol. 25(4), pages 545-554.
    3. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M., 2007. "Wind power resource assessment for Rafha, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 937-950, June.
    4. Rehman, Shafiqur, 2005. "Prospects of wind farm development in Saudi Arabia," Renewable Energy, Elsevier, vol. 30(3), pages 447-463.
    5. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M. & Shash, A., 2007. "Feasibility study of hybrid retrofits to an isolated off-grid diesel power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(4), pages 635-653, May.
    6. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    7. Rehman, Shafiqur & Al-Abbadi, Naif M., 2007. "Wind shear coefficients and energy yield for Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(5), pages 738-749.
    8. Rehman, Shafiqur & Ahmad, Aftab, 2004. "Assessment of wind energy potential for coastal locations of the Kingdom of Saudi Arabia," Energy, Elsevier, vol. 29(8), pages 1105-1115.
    9. M. Mujahid Rafique & Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems, 2018. "Feasibility of a 100 MW Installed Capacity Wind Farm for Different Climatic Conditions," Energies, MDPI, vol. 11(8), pages 1-18, August.
    10. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
    11. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    12. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    13. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    14. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    15. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    16. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    17. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    18. Ivan Marović & Ivana Sušanj & Nevenka Ožanić, 2017. "Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    19. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    20. Zarzo, Manuel & Martí, Pau, 2011. "Modeling the variability of solar radiation data among weather stations by means of principal components analysis," Applied Energy, Elsevier, vol. 88(8), pages 2775-2784, August.

    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:29:y:2004:i:6:p:939-947. 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.