A new method for wind speed forecasting based on empirical mode decomposition and improved persistence approach

Abstract

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 persistence approach 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 Moving Average method (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. A set of measured wind speed data from a given wind farm locating at Jiangsu Province in China were modeled using the proposed method and the forecasted results were compared to the measured wind speeds as well as those predicted with other traditional methods. The results indicate that the forecasting precision can be improved with the developed model.