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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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Accurate short-term prediction of wind speed is one of the critical issues faced by wind farm industry so as to plan trading strategies and managing power distribution. In this paper, we demonstrate that empirical mode decomposition (EMD) of the wind speed time series significantly improves prediction accuracy of nonlinear prediction tools. While EMD technique is used to decompose the measured wind speed time series data into its basic components called intrinsic mode functions and residue, nonlinear prediction tool is used to model and forecast each component. Prediction result of each component is summed up to reconstruct the wind speed data into its original form. The Resultant prediction of this hybrid method is compared with the new reference forecast method (NRFM) and local first order method (LFO). The comparison results demonstrate that, prediction accuracy can be remarkably improved by combining EMD and nonlinear model.

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