Wind speed prediction based on empirical mode decomposition and improved LS-SVM

Wind speed forecasting plays an important role in sizing the capacity of the energy storage system and guaranteeing the security and stability of power system. In order to forecast wind speeds more accurately, a hybrid forecasting method based on empirical mode decomposition (EMD) and an improved least square support vector machine mode (LSSVM) has been proposed in this paper. Employing the EMD technique to decompose the measured wind speeds into many intrinsic mode function (IMF) components and a residue, which represent the original signal in both high-frequency and low-frequency signals. Meanwhile each IMF is analyzed and predicted using LS-SVM (high-frequency signals) and Persistence Approach (low-frequency signals), so does the residue. The sum of the predictive value for each decomposed component is the forecasted data. The proposed method was applied to the modeling and forecasting of a set of data from a given wind farm in Jiangsu Province, China. The results demonstrate the validity and practicability of the novel method. The forecasted results were compared to the measured values as well as those predicted with other traditional methods. The results indicate that the forecasting precision can be improved with the developed model.