改进的模糊聚类算法在负荷预测中的应用  

Application of Improving Fuzzy Clustering Algorithm to Power Load Forecasting

作　　者：陈柔伊[1] 张尧[1] 武志刚[1] 陈泽淮[1] 

机构地区：[1]华南理工大学电力学院,广州510640

出　　处：《电力系统及其自动化学报》2005年第3期73-77,共5页Proceedings of the CSU-EPSA

摘　　要：针对传统的c均值模糊聚类算法易陷入局部最优解、初始值c值的给定存在着很大的人为因素以及在整个计算过程中无法自我调节的缺陷,利用遗传算法的全局寻优能力并采用一种新式的双码染色体编码方法对传统的c均值模糊聚类算法进行了改进,同时将这一自适应的SFGO(SamplingFuzzyc-meanswithGeneticOptimization)算法运用到电力系统的中长期负荷预测中,得到了比较好的效果。Classical fuzzy c means have some drawbacks such as unsatisfactory performance in finding global optimum,difficulty in choosing initial c value and lack of self-adaptive ability.To overcome these drawbacks,this paper proposes an improved fuzzy c means algorithm named self-adaptive SFGO,which takes the advantage of GA in finding global optimal solution.A new coding method called double chromosome code is used in this algorithm.Case studies verify that this algorithm can yield good forecasting results for long-term power loadforecasting problems.

关 键 词：自适应c均值模糊聚类法 双码染色体 遗传算法 电力负荷预测 

分 类 号：TM715[电气工程—电力系统及自动化]