摘要
提出一种基于相空间重构和进化高斯过程的短期风速预测方法。首先,运用自相关法和假近邻法分别得出原始风速时间序列的延迟时间和嵌入维数,实现混沌风速时间序列的相空间重构;然后,运用进化高斯过程回归模型进行建模,通过高斯过程模型确定输入量和输出量之间的关系,并用改进粒子群算法求取最优超参数。根据某实测风速数据进行了风速预测,结果表明本文所提出的方法能有效提高风速预测精度。
A short-term wind speed forecasting method based on phase-space reconstruction and evolutionary Gaussian process model is proposed in this paper .Firstly, the autocorrelation method and false nearest neighbor method are applied to calculate the delay time and embedding dimension of the wind speed time series , which are used to accomplish the phase-space reconstruction of the chaotic wind speed time series .Secondly, the evolutionary Gaussian process model , which combines Gaussian process with evolutionary algorithm , is used to forcast the wind speed .This model uses Gaussian process model to determine the relationship between the input and output variables , and the improved PSO algorithm to optimize the hyper parameters .The prediction results show that the proposed method can improve the prediction accuracy .
出处
《计算机与现代化》
2016年第7期33-36,43,共5页
Computer and Modernization
基金
安徽省自然科学基金资助项目(1308085QF103)
安徽省教育厅自然科学基金资助项目(KJ2013B073)
关键词
风速预测
短期
相空间重构
进化高斯过程
改进粒子群算法
wind speed forecast
short-term
phase-space reconstruction
evolutionary Gaussian process
improved PSO algo-rithm
作者简介
常纯（1989-），女，安徽安庆人，苏州大学艺术学院助教，硕士，研究方向：人工智能与模式识别，风速及风功率预测。
