Short-term Wind Power Forecast Based on GA-Elman Neural Network and Nonlinear Combination Model

Abstract

The accuracy of short-term wind power forecast is important to the operation of power system. Based on the real-time wind power data, a wind power prediction model using Elman neural network is proposed. In order to overcome such disadvantages of Elman neural network as easily falling into local minimum, this paper put forward using Genetic algorithm (GA) to optimize the weight and threshold of Elman neural network. At the same time, it's advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for generalized regression neural network (GRNN) to do nonlinear combination forecasting. By analyzing the measured data of wind farms, indicate that the nonlinear combination of forecasting model can improve forecast accuracy.