Application of Wind Power Prediction Based on Particle Swarm Optimization K-Means Clustering Algorithm
Because of the randomness,gap property and volatility of wind,it is necessary to predict wind power in order to improve the stability of power grid network operation.In the actual wind turbine operation due to the restrictions of geographic environmental factors,so traditional wind power forecasting method is no longer applicable.The accuracy of predicting wind power by using K-means clustering algorithm is increased,but due to the random K-means clustering center selection,there are still a lot of defects.This paper proposes a wind power prediction by using Particle Swarm Optimization (pso) and K-means clustering algorithm.The simulation results show that the accuracy of wind power prediction by using Particle Swarm Optimization and K-means clustering algorithm is better than traditional method and K-means clustering algorithm.
NETL
NSTL
万方数据
维普
