Short Term Load Forecasting Using Chaotic Analysis Algorithm for Neural Network

摘要：

By analyzing with chaotic theory, the power load time series belongs to chaotic series. The time series of power load is reconfigured in the phase space, the largest Lyapunov exponents are calculated. BP neural network model based on chaotic analysis is set up in the phase space. The model is applied on short term forecasting of daily power load which has chaotic characteristics. The forecasting result of common BP algorithm and chaotic analysis algorithm is Compared, the results show that the neural network model based on chaotic analysis has higher precision.

关键词： short term lord forecasts Lyapunov exponents Chaos analysis BP Neural Network Phase space reconfiguration theory

作者: ZHANG Qing ZHANG Li

作者单位: Mechanical and Electrical Engineering Institute,Agricultural University of Hebei,P.R.China,071000

会议类型: 国际会议

会议名称: 2007 International Conference on Agriculture Engineering(2007年农业工程国际会议)

会议地点: 河北保定

会议语种:英文

页码: 366-370

在线出版日期: 2007-10-20（万方平台首次上网日期，不代表论文的发表时间）