Hybrid Case-Based Reasoning System for Short-Term Load Forecasting
Short-term load forecasting plays a significant role in the electric power system.In this paper,an advanced approach based on Case-based Reasoning theory is proposed to help solve the STLF problem with the aid of rough sets information entropy and principal component analysis methods which is mainiv applied to reduce the attributes of load cases and dispose the essentiality and relativity of load data.As a result,the training time in the process of retrieval decreased,and the effective control is executed aiming at petit factors to essential ones.Finally.it is performed on the data of Bao Ding Electric Power Company(BDEPC)during 2000-2004,and the testing result indicated that the presented model is feasible and promising for load forecasting.
short-term load forecasting case-based reasoning rough set information entropy principal component analysis
Jinsha Yuan Li Qu Weihua Zhang Li Li
国际会议
The International Conference Information Computing and Automation(2007国际信息计算与自动化会议)
成都
英文
1085-1087
2007-12-19（万方平台首次上网日期，不代表论文的发表时间）