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Others still forecast wind speed and wind production.
For instance, Yousefi et al. (2015) used ANN to model the nonlinearity of wind speed to accurately forecast wind speed in wind farms.
Perez et al. [15] forecast wind speed using a blended ensemble, which consists of the Weather Research and Forecasting Single Column Model and time series forecasts that are calibrated with Bayesian model averaging.
With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed.
Reference [10] used the discrete Hilbert transform as minimum phase type filter to forecast wind speed and identify the characterization of wind speed.
The former is used to forecast wind speed based on numerical weather report while the latter uses historical wind speed to extrapolate.
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It is assumed that the system unit commitment schedule as well as the load forecasting data and wind speed forecast data are known, and then the wind turbine output can be calculated.
However, the correlation among different forecasting steps is often neglected in current multi-step ahead wind speed forecasting approaches, and the characteristic of heteroscedasticity in wind speed forecasting errors is usually not taken into consideration.
The effectiveness of the proposed forecast strategy to predict wind speed is evaluated by the trials of 10 min ahead wind speed forecasting at two locations of the National Renewable Energy Laboratory NRELL).
Many wind speed forecasting algorithms have been proposed to improve prediction accuracy.
Thus, a correlation aware multi-step ahead wind speed forecasting technique with heteroscedastic multi-kernel learning is designed.
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