基于CEEMD和混沌理论的超短期风电功率预测模型

doi:10.16182/j.issn1004731x.joss.201804043

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (4): 1560-1565.doi: 10.16182/j.issn1004731x.joss.201804043

基于CEEMD和混沌理论的超短期风电功率预测模型

王丽婕¹, 张利¹, 张岩²

1. 北京信息科技大学自动化学院,北京 100192;
2. 国网北京电力科学研究院,北京 100045

收稿日期:2016-05-03 修回日期:2016-09-09 出版日期:2018-04-08 发布日期:2019-01-04
作者简介:王丽婕(1983-),女,湖北枣阳,博士,副教授,研究方向为系统建模与优化。
基金资助:
国家自然科学基金(51607009),北京市教委科技计划面上项目(KM201511232007),北京市属高等学校高层次人才引进与培养计划(CIT&TCD20 1404126)

Ultra-short-term Wind Power Forecasting Based on CEEMD and Chaos Theory

Wang Lijie¹, Zhang Li¹, Zhang Yan²

1. School of Automation, Beijing Information Science and Technology University, Beijing 100192 China;
2. State Grid Beijing Electric Power Research Institute, Beijing 100045, China

Received:2016-05-03 Revised:2016-09-09 Online:2018-04-08 Published:2019-01-04

摘要/Abstract

摘要： 提出一种基于互补集合经验模态分解和混沌理论的风电功率超短期预测的方法。首先对风电功率时间序列进行互补集合经验模态分解(Complementary Ensemble Empirical Mode Decomposition, CEEMD),以降低序列的非平稳性,CEEMD可以很好地克服经验模态分解的模态混叠问题;利用混沌相空间重构方法对各子序列的特征进行提取,为建模时输入维数的选取提供依据;利用最小二乘支持向量机方法对各子序列分别建模及预测;将预测结果叠加起来。通过对内蒙古某风电场的发电功率进行预测,证实了该模型的有效性,与传统的经验模态分解和神经网络结合的模型相比较,平均绝对误差减小了3.8%。

关键词: 风功率, 预测, 互补集合经验模态分解, 最小二乘支持向量机, 相空间重构

Abstract: This paper studies the ultra-short-term prediction of wind power generating capacity by means of CEEMD and chaos theory. Wind power time series is decomposed by CEEMD to decrease the non-stationary of time series. CEEMD can overcome the modal aliasing problem of EMD. The phase space reconstruction method is used to extract characteristics of each sequence, which provides the basis for the selection of input dimension when building a model. The least squares support vector machine models are built for each sequence and the prediction are made separately. The predicted results are added to get the final prediction. Simulation is performed to the real data from a wind farm of Inner Mongolia. The results show that the proposed method is effective and the mean absolute error decreases by 3.8% compared with the conventional EMD and neural network model.

Key words: wind power, forecasting, CEEMD, least squares support vector machine, phase space reconstruction

中图分类号:

TP391.9

王丽婕, 张利, 张岩. 基于CEEMD和混沌理论的超短期风电功率预测模型[J]. 系统仿真学报, 2018, 30(4): 1560-1565.

Wang Lijie, Zhang Li, Zhang Yan. Ultra-short-term Wind Power Forecasting Based on CEEMD and Chaos Theory[J]. Journal of System Simulation, 2018, 30(4): 1560-1565.

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基于CEEMD和混沌理论的超短期风电功率预测模型

Ultra-short-term Wind Power Forecasting Based on CEEMD and Chaos Theory

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