中国电力2011，Vol.44Issue(11)：73-77,5.
基于相空间重构的神经网络短期风电预测模型
Neural network model for short-term wind power prediction based on phase space reconstruction
牛晨光 1刘丛2
作者信息
- 1. 华北电力大学电气与电子工程学院,河北保定071003
- 2. 北京供电局,北京100031
- 折叠
摘要
Abstract
With the lasting rapid increase of wind turbine capacity and the large-scale wind farm construction, the degree of recognition on wind power in different countries or regions' power grids is also increased. The short-term wind farm generation prediction plays an essential role for the wind farm access and power grid dispatch. The analysis on time series of wind farm generating capacity shows its chaos characteristic. Based on chaotic theory, phase space reconstruction method is used in RBF neural network and BP neural network to predict wind farm generation in short-term. The comparison and analysis of the prediction results show that RBF neural network has a more accurate prediction.关键词
混沌理论/相空间重构/短期风电功率预测/RBF神经网络/BP神经网络Key words
chaotic theory/phase space reconstruction/short-term wind power prediction/RBF neural network/BP neural network分类
信息技术与安全科学引用本文复制引用
牛晨光,刘丛..基于相空间重构的神经网络短期风电预测模型[J].中国电力,2011,44(11):73-77,5.
