基于EEMD-BBO-ELM的短期风电功率预测方法

基于EEMD-BBO-ELM的短期风电功率预测方法
摘要:对风电功率进行短期预测是降低风电不确定性对电力系统稳定运行影响最主要的手段之一。针对短期风电的功率预测，提出了一种基于集合经验模态分解算法(ensemble empirical mode decomposition，EEMD)、生物地理学优化算法(biogeography-based optimization，BBO)和极限学习机(extreme learning machine, ELM)算法的风电功率短期的预测方法(EEMD-BBO-ELM)。首先，利用EEMD算法对原始风电功率序列进行分解；然后，利用BBO算法优化后的ELM算法进行预测；最后，利用实测数据验证可知本文算法的预测性能优秀，收敛速度快，具有较高的工程利用价值。

Abstract：The short-term prediction of wind power is one of the most important means to reduce the influence of wind power uncertainty on the stable operation of the power system. This paper proposes a short-term prediction of wind power based on the ensemble empirical mode decomposition (EEMD) algorithm, the biogeography-based optimization (BBO) algorithm and the extreme learning machine (ELM) algorithm (EEMD-BBO-ELM). First, the EEMD algorithm is used to decompose the original wind power sequence, and then the ELM algorithm optimized by BBO is used to predict the power sequence. Finally, the experimental data verifies that the prediction performance of this algorithm is excellent, the convergence speed is fast, which has high engineering value.

标题：基于EEMD-BBO-ELM的短期风电功率预测方法
title:Short-Term Prediction of Wind Power Based on EEMD-Optimal-ELM Algorithm

作者：时彤, 杨朔
authors:SHI Tong,YANG  Shuo

关键词：风电功率预测,集合经验模态分解(EEMD),生物地理学优化算法(BBO),极限学习机(ELM),
keywords:wind power prediction,EEMD,BBO,ELM,

发表日期：2018-09-27

2025-1-21 20:06 上传 基于EEMD-BBO-ELM的短期风电功率预测方法.pdf

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