Short term wind power prediction based on improved quantum particle swarm optimization algorithm and kernel extreme learning machine

Abstract

In order to improve the prediction accuracy of wind power prediction model, a short-term wind power prediction model based on improved quantum particle swarm optimization kernel extreme learning machine is proposed. The differential evolution algorithm is introduced into the standard quantum particle swarm optimization algorithm to enrich the diversity of the search population. In addition, the Gaussian perturbation strategy is used to increase the local optimization ability of the algorithm. The improved quantum particle swarm optimization algorithm is used to optimize the parameters of the kernel extreme learning machine. Finally, the short-term wind power prediction model is established by using the optimized KELM and the actual wind farm data. The results show that the proposed model has high prediction accuracy.