An Iterative Dynamic Ensemble Weighting Approach for Ultra-short-term Wind Power Prediction

With the increasing of wind power installed capacity, the fluctuation and uncertainty of wind bring huge risks and challenges to the safe and steady operation of power system, while ultra-short-term wind power forecasting is an effective mitigation measure. In the last ten years, a multitude of studies has grown up around the theme of prediction accuracy, while most of them have ignored the effect of input time step on the prediction results, which makes the prediction accuracy have certain geographical limitations. For the sake of solving these problems and ensure that prediction model can make a more accurate judgment on the changes in wind power output, an iterative dynamic integration weighting method is proposed. First, the input step size of the rolling forecast is selected to determine the input with promising performance. Then, compared with other general models by using several error indicators. We have carried out several sets of experiments to verify the availability of this model. The consequence of the experiment demonstrate that the proposed model has better performance, especially in the case of rapid fluctuations of wind power.