电力系统及其自动化学报2017，Vol.29Issue(9)：65-69,5.DOI:10.3969/j.issn.1003-8930.2017.09.011
基于相空间重构和误差补偿的风电功率混沌时间序列预测模型
Chaotic Time Series Prediction Model of Wind Power Based on Phase Space Reconstruction and Error Correction
王兰 1王晞 2李华强 1刁芳钰1
作者信息
- 1. 四川大学电气信息学院智能电网四川省重点实验室,成都 610065
- 2. 国网四川省电力公司经济技术研究院,成都 610041
- 折叠
摘要
Abstract
Considering that wind power time series shows short-term predictability in the multi-dimensional space be?cause of its chaotic characteristic,a forecasting model is proposed by combining an error correction model with phase space reconstruction theory,thus the error correction model is expanded to the multi-dimensional space. The proposed model uses vector auto-regression(VAR)model and Elman neural network model to predict the linear and nonlinear characteristics respectively,because the measured wind power data contain both linear and nonlinear characteristics. In this way,the final forecasting value can be obtained by combining the results of the two models. The simulation results with the data from one wind farm in China show that compared with the single linear model and single nonlinear model , the proposed model has higher prediction accuracy.关键词
风电功率预测/相空间重构/向量自回归/Elman神经网络Key words
wind power prediction/phase space reconstruction/vector auto-regression(VAR)/Elman neutral network分类
信息技术与安全科学引用本文复制引用
王兰,王晞,李华强,刁芳钰..基于相空间重构和误差补偿的风电功率混沌时间序列预测模型[J].电力系统及其自动化学报,2017,29(9):65-69,5.
