Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for short-term prediction of wind speed and wind power is proposed, which is based on singular spectrum analysis (SSA) and locality-sensitive hashing (LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend, which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted for prediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.