Short Term Prediction Method for Output Power of Wind Farm Based on Fusion Model

Abstract

Wind power has the characteristics of randomness and intermittent, the safe and stable operation of power system will be influenced after a large scale of wind power integrated to the system. Against this background, it is extremely important for the power grid operation to forecast the power output of wind farm. A fusion prediction model based on wind speed and wind power is proposed in this paper in order to improve the precision of the short term output wind power. Firstly, a combined prediction model of wind power is constructed by autoregressive time series and generalized regression neural network to forecast the wind power directly. Then, the same combined prediction model is used to forecast the wind speed and the wind power is computed indirectly according to the relationship between wind speed and power. Finally, the fusion prediction model can be obtained by combining the two previous model. As a result, it can improve the prediction accuracy of output wind power for large scale wind farm effectively.