模糊关联规则挖掘在电力负荷预测中的应用  

Application of Fuzzy Association Rule Mining on Electric Load Prediction

作　　者：沈海澜[1] 王加阳[1] 蒋外文[1] 陈再良[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《计算机工程》2003年第15期138-140,162,共4页Computer Engineering

摘　　要：提出了一种基于模糊关联规则挖掘的电力负荷预测新方法,采用模糊c-均值算法对连续型属性域上的历史数据进行分类并模糊化,应用文中提出的模糊关联规则挖掘算法挖掘出电力负荷量与其相关环境变量间潜在的有效模糊关联规则。利用这些规则进行匹配预测,得到电力负荷量模糊化的预测结果;最后将其反模糊化,得出预测值。给出了实验仿真结果,表明了该方法的有效性。A new method of electric load prediction based on fuzzy association rule mining is presented in this paper. The continuous valued data is classed and mapped into fuzzy value by fuzzy c-means algorithm in this paper. According to the fuzzy associstion rules mining algorithm provided in this paper, the effective fuzzy association rules between the electric load attribute and its related conditional attributes can be mined. Those mined rules are used for prediction to get fuzzy value of electric load,and then transform fuzzy value to predictictive value.The simulation results show the validity of the method.

关 键 词：电力负荷预测 模糊关联规则挖掘 模糊C-均值算法 

分 类 号：TP311.132[自动化与计算机技术—计算机软件与理论]