Wind speed and power forecasting based on RBF neural network

As a renewable and clean energy source, wind power is being widely utilized all over the world. The uncertainty of wind speed, however, makes certain trouble for the development of wind power generation. In order to relieve the disadvantageous impact of wind speed intermittence on the connected power system, the wind power forecasting needs to be carried out. In this paper, a wind speed and power forecasting method based on RBF neural network is proposed. In which, the influence of the dataset construct method on the forecasting accuracy is researched. The simulation results show that the forecasting accuracy is improved by performing the dataset reconstruction. And it is proved that the higher forecasting accuracy of wind power can be gotten through introducing the wind speed as RBF inputs.