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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 32, issue 11
Ann. Geophys., 32, 1415–1425, 2014
https://doi.org/10.5194/angeo-32-1415-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 32, 1415–1425, 2014
https://doi.org/10.5194/angeo-32-1415-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular paper 19 Nov 2014

Regular paper | 19 Nov 2014

Deterministic prediction of surface wind speed variations

G. V. Drisya1, D. C. Kiplangat2,1, K. Asokan3, and K. Satheesh Kumar1 G. V. Drisya et al.
  • 1Department of Futures Studies, University of Kerala, Thiruvananthapuram, Kerala, India
  • 2Department of Statistics & Actuarial Science, Dedan Kimathi University of Technology – Box 657-10100, Nyeri, Kenya
  • 3Department of Mathematics, College of Engineering, Thiruvananthapuram, Kerala, India

Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

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We demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data. The predictions are remarkably accurate up to 1 hour and reasonably accurate up to 3 hours. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully.
We demonstrate that deterministic forecasting methods can make accurate short-term predictions...
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