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基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型
引用本文:张学清,梁军.基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型[J].物理学报,2013,62(5):50505-050505.
作者姓名:张学清  梁军
作者单位:1. 山东大学电气工程学院, 济南 250061; 2. 电网智能化调度与控制教育部重点实验室(山东大学), 济南 250061
基金项目:国家自然科学基金(批准号: 51177091)和山东省自然科学基金(批准号: ZR2010EM055)资助的课题.
摘    要:针对风电功率时间序列的混沌特性,提出了一种基于集成经验模态分解(ensemble empirical mode decomposition, EEMD)-近似熵和回声状态网络(echo state network, ESN) 的风电功率混沌时间序列组合预测模型.首先为降低对风电功率局部分析的计算规模以及提高预测的准确性, 利用EEMD-近似熵将风电功率时间序列分解为一系列复杂度差异明显的风电子序列; 然后对各子序列分别建立ESN、经过高频分量正则化改进的EEMD-ESN模型和最小二乘支持向量机预测模型; 最后以某一风电场实际采集的数据为算例,仿真结果表明EEMD-ESN模型在训练速度和预测精度上优于最小二乘支持向量机模型,为实现风电功率短期预测的在线工程应用提供了新的有益参考. 关键词: 混沌时间序列 风电预测 集成经验模态分解 近似熵

关 键 词:混沌时间序列  风电预测  集成经验模态分解  近似熵
收稿时间:2012-08-20

Chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-approximate entropy and reservoir
Zhang Xue-Qing,Liang Jun.Chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-approximate entropy and reservoir[J].Acta Physica Sinica,2013,62(5):50505-050505.
Authors:Zhang Xue-Qing  Liang Jun
Institution:1. School of Electrical Engineering, Shandong University, Jinan 250061, China;2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, China
Abstract:According to the chaotic feature of wind power time series, a combined short-term wind power forecasting approach based on ensemble empirical mode decomposition (EEMD)-approximate entropy and echo state network (ESN) is proposed. Firstly, in order to reduce the calculation scale of partial analysis for wind power and improve the wind power prediction accuracy, the wind power time series is decomposed into a series of wind power subsequences with obvious differences in complex degree by using EEMD-approximate entropy. Then, the forecasting model of each subsequence is created with least squares support vector machine (LSSVM), ESN and EEMD-ESN improved with the regularized high frequency parts. Finally, the simulation is performed by using the real data collected from a certain wind farm, the results show that the EEMD-ESN model is better in the training speed and forecasting accuracy, than those obtained from the least square support vector machine (LSSVM) model, which provides a new useful reference for the short-term forecasting of wind power in online engineering application.
Keywords:chaotic time series  wind power prediction  ensemble empirical mode decomposition  approximate entropy
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