Super short-term prediction of wind power based on EEMD-PSO-ELM
Addressing the problem of low wind power prediction accuracy caused by the unstable characteristics of wind farm power,a super-short-term wind power prediction method based on ensemble empirical mode decomposition(EEMD),particle swarm optimization(PSO),and extreme learning machine(ELM)is proposed.Firstly,the wind power sequence is decomposed into several modes using EEMD to avoid mode aliasing.Secondly,phase space reconstruction is used to calculate the Hurst exponent for the decomposed modes,and the optimal sub-sequence is obtained according to the Hurst exponent.Finally,the PSO-ELM model predicts the wind power for the optimal sub-sequence.Experimental results from a specific wind farm illustrate that the EEMD-PSO-ELM prediction model achieves higher accuracy in wind power forecasting.
wind farm powerEEMDphase space reconstructionsuper short termprediction accuracy
国家科技期刊平台
NSTL
万方数据
维普
