Short-term Load Prediction Based on Chaos Time Series Theory

摘要：

In this paper,two chaotic predicted methods are applied to forecast the grids load data.The data are collected from the grid of New South Wales,Australia.It records the grids load of four weekends in May.First,the phase space is reconstructed using the delay embedding theorem suggested by TAKENS.Second,for reducing the negative influence of the Largest Lyapunov Exponent Method,a method based on the Adding-weighted Largest Lyapunov Exponent Method is proposed.Then the Adding weighted One-rank Local-region Forecasting Method as a traditional chaotic forecasting arithmetic is used to forecast the load.Finally,we compared the two methods.Results presented show that the proposed Adding-weighted Largest Lyapunov Exponent Method appears to perform better than the traditional chaotic forecasting arithmetic.

关键词： Chaotic forecasting reconstruction of the phase space Adding-weighted Largest Lyapunov Exponent Method Adding-weighted One-rank Local-region Forecasting Method

作者: Hongjie Wang Dezhong Chi

作者单位: Railway Technical College Lanzhou Jiaotong University Lanzhou 730000,China School of Mathematics & Statistics Lanzhou University Lanzhou 730000, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 1141-1144

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）