Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

Abstract

Wind power ramp forecasting is very significant for grid integration of large wind energy. A ramp event is defined as the sharp increase or decrease of wind power on a large scale in short time. A methodology for wind power ramp forecasting is described. The method is based on Least Square Support Vector Machine (LSSVM) and the definition of ramp events by filtering the original signal. The performance of the proposed model is evaluated on a wind farm in China, which shows that LSSVM model is competent in forecasting wind power ramp events.