Short-term Wind Power Interval Prediction Based on Numerical Weather Forecast Correction and Meteorological Similar Days
In order to solve the problem that wind power output cannot be accurately predicted due to its fluctuation,a short-term wind power output interval prediction method based on improved BP neural network and Bootstrap method was proposed.The historical numerical weather forecast(NWP)and the actual observation values were fitted,and the NWP data of the predicted time were corrected,and the similar meteorological time was screened according to the grey correlation coefficient method.The parameters of BP neural network were optimized by particle swarm optimization algorithm,and the Bootstrap method was introduced to increase the data diversity,and several deterministic prediction models were established.The percentile estimation method is used to obtain the power fluctuation interval at a given confidence level.Taking a domestic wind farm as an example,the wind power is predicted 24h in advance,and the results show that all the evaluation indexes in the obtained interval meet the practical engineering needs.
the wind speed correctionsimilar daythe Bootstrap methodBP neural networkinterval prediction
万方数据
维普
