Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model

Abstract

Wind power may present undesirable discontinuities and fluctuations due to considerable variations in wind speed, which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. Variations in wind power cannot be sufficiently estimated by persistence type basic forecasting methods particularly in medium and long terms. Therefore a new statistical method is presented here in this paper based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.