宁夏电力Issue(2)：1-5,26,6.DOI:10.3969/j.issn.1672-3643.2024.02.001
基于EEMD-PSO-ELM的风电功率超短期预测
Super short-term prediction of wind power based on EEMD-PSO-ELM
毛元 1冯洋 2严岩 3陈磊 3钱勇3
作者信息
- 1. 国网南京市江北新区供电公司,江苏 南京 211800
- 2. 国网宁夏电力有限公司培训中心,宁夏 银川 750011
- 3. 国网宁夏电力有限公司电力科学研究院,宁夏 银川 750011
- 折叠
摘要
Abstract
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.关键词
风电场功率/集合经验模态分解/相空间重构/超短期/预测精度Key words
wind farm power/EEMD/phase space reconstruction/super short term/prediction accuracy分类
信息技术与安全科学引用本文复制引用
毛元,冯洋,严岩,陈磊,钱勇..基于EEMD-PSO-ELM的风电功率超短期预测[J].宁夏电力,2024,(2):1-5,26,6.
