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Wind speed prediction using statistical regression and neural network

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Abstract

Prediction of wind speed in the atmospheric boundary layer is important for wind energy assessment, satellite launching and aviation, etc. There are a few techniques available for wind speed prediction, which require a minimum number of input parameters. Four different statistical techniques, viz., curve fitting, Auto Regressive Integrated Moving Average Model (ARIMA), extrapolation with periodic function and Artificial Neural Networks (ANN) are employed to predict wind speed. These methods require wind speeds of previous hours as input. It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods, viz., extrapolation using periodic curve fitting and ANN and the other two methods are not very useful.

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Authors and Affiliations

  1. Department of Atmospheric and Space Sciences, University of Pune, Pune, 411 007, India

    Makarand A. Kulkarni & P. N. Sen

  2. Department of Physics, University of Pune, Pune, 411 007, India

    Sunil Patil

  3. Meteorological Facility, Sriharikota 524 124, Nellore (Dt), A.P., India

    G. V. Rama

Authors
  1. Makarand A. Kulkarni
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  2. Sunil Patil
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  3. G. V. Rama
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  4. P. N. Sen
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Corresponding author

Correspondence to Makarand A. Kulkarni.

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Kulkarni, M.A., Patil, S., Rama, G.V. et al. Wind speed prediction using statistical regression and neural network. J Earth Syst Sci 117, 457–463 (2008). https://doi.org/10.1007/s12040-008-0045-7

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  • DOI: https://doi.org/10.1007/s12040-008-0045-7

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