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短期负荷预测最大Lyapunov指数预报模式预测值的判定
引用本文:杜杰,陆金桂,曹一家. 短期负荷预测最大Lyapunov指数预报模式预测值的判定[J]. 电网技术, 2006, 30(20): 20-24
作者姓名:杜杰  陆金桂  曹一家
作者单位:南京工业大学,自动化学院,江苏省,南京市,210009;浙江大学,电气工程学院,浙江省,杭州市,310027
基金项目:国家自然科学基金,教育部高等学校博士学科点专项科研基金
摘    要:首先分析了相空间中混沌吸引子邻近轨道间的平行、交叉、折叠3种拓扑关系,根据负荷吸引子的特点提出了负荷时序最大Lyapunov指数预报模式预测值的判定依据,并探讨了相空间中临近点(轨道)的选择方法。仿真试验表明改进的负荷预测建模策略避免了原有Lyapunov指数预报模式预测值选择的盲目性,吸引子临近点的选择符合电力负荷数据的特点,所建立的短期负荷预测模型提高了预测精度并达到了预期效果。

关 键 词:短期负荷预测  最大Lyapunov指数预报模式  混沌吸引子  预测值判定
文章编号:1000-3673(2006)20-0020-05
收稿时间:2006-04-12
修稿时间:2006-04-12

Determination of Forecasted Values of Short-Term Load Forecasting Model Based on Largest Lyapunov Exponent
DU Jie,LU Jin-gui,CAO Yi-jia. Determination of Forecasted Values of Short-Term Load Forecasting Model Based on Largest Lyapunov Exponent[J]. Power System Technology, 2006, 30(20): 20-24
Authors:DU Jie  LU Jin-gui  CAO Yi-jia
Affiliation:1. College of Automation, Nanjing University of Technology, Nanjing 210009, Jiangsu Province, China; 2. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang Province, China
Abstract:The authors fistly analyze three kinds of topological properties of the adjacent orbits including approximate parallel,overlap and fold in the phase space in this paper;then according to the feature of load attractor and based on load series,a criterion for the choice of the forecasted results from the forecasting mode using the largest Lyapunov exponent is proposed.In addition,a selection method of adjacent orbit in the phase space of load attractor is also researched.Simulation results show that the improved load forecasting modeling strategy can avoid the blindness during the choice of forecasted value in original Lyapunov exponent forecasting mode,the selection of adjacent point of the load attractor is in accordance with the feature of power load series,and the established short-term load forecasting model can improve the accuracy of forecasting.
Keywords:short-term electric load forecasting  largest Lyapunov exponent forecasting model  chaotic attractor  determination of forecasting values
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