In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.
When basic particle swarm optimization algorithm (PSO) is used to resolve some complex problems, its global optimal model usually falls into local optimal value and its local model has slowest convergence velocity in the later stage of evolution. So, a simplified particle swarm optimization algorithm is proposed. Firstly, all particles in whole swarm are divided into three categories, denoted as the better particles, the ordinary particles and the worse particles according to their fitness. After the velocity equation of PSO is analyzed, the velocity part of PSO's iteration equations is removed rationally. Then, these three types of particles evolve dynamically according to three corresponding kinds of simplified algorithm models. Then, PSO, other two improved PSOs with good optimization performance at present and simplified PSO proposed by this paper all are used to resolve the optimization problems of four widely used test functions, and the results show that simplified PSO has better optimization performance than others.
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