电测与仪表Issue(24)：6-10,5.
相空间重构和支持向量机结合的电力负荷预测模型研究
Study on power load forecasting model based on phase space reconstruction and SVM
摘要
Abstract
The impacts of centralized operation of wind turbines on grid′s security and stability operation ask for high requirements for power load forecasting precision in order to realized reasonable planning efficient operation of various power supply units.The time series of grid has chaotic characteristics and it is difficult to describe its characteristics and inherent laws.The chaotic phase space reconstruction theory is adopted to study the power load time series sample data.Time delay and embedding dimension are obtained through the mutual information method and the CAO.Lya-punov exponent of this system is obtained so as to prove that the grid system has chaotic characteristics.Then the phase space is reconstructed according to the time delay and embedding dimension.On the basis of phase space re-construction, support vector regression algorithm is adoped to predict the power load.The grid search method is used for parameter optimization.Finally, the predicted results with the time series prediction model and BP neural network model are compared.The results show that is the proposed method is a high precison load forecasting method with small error.关键词
支持向量回归/混沌/相空间重构/电力负荷预测Key words
chaos theory/phase space reconstruction/support vector regression/power load forecasting分类
信息技术与安全科学引用本文复制引用
李昕,闫宏伟,马弘毅..相空间重构和支持向量机结合的电力负荷预测模型研究[J].电测与仪表,2014,(24):6-10,5.基金项目
山西省自然科学基金资助项目 （）
