Gaussian mixture model-based neural network for short-term wind power forecast

The wind power forecast has attracted much attention in recent years because of the significantly increasing number of large-scale integrations of the wind power plants in the electric grid. In this paper, a Gaussian mixture model-based neural network model is proposed to forecast the short-term wind power generation. Actual measured wind power output data are adopted to train the proposed model. Test results of wind power obtained by autoregressive integrated moving average, radial basis function neural network, support vector regression, and the proposed method are then under comparisons. It shows that the proposed method can provide more accurate forecast of short-term wind power output than other compared methods.