Short-Term Local Prediction of Wind Power Based on Singular Spectrum Analysis and Self-Organizing Maps

Along with the increasing penetration of wind power into power systems, more accurate forecast of wind power becomes more and more important for real-time scheduling and operation. This paper proposes a novel model for short-term wind power forecast based on singular spectrum analysis (SSA) and self-organizing maps (SOM). In order to deal with the impact of high volatility of the original time series, SSA is utilized to extract the mean trend from the original time series. After that, SOM is applied to select the similar segments from mean trend, which are then employed in local prediction by support vector regression (SVR). Simulation studies are conducted on real wind power time series, and the final results indicate that the proposed model is more accurate and stable than other models.