摘要
对电力系统客户的精确分类可为客户提供良好的差异化管理和个性化服务.针对客户分类问题,提出了一种基于均衡优化与极限学习机的分类方法.该方法中提出了一种自适应竞争机制来平衡均衡优化的全局探索与局部挖掘能力,从而有效提升了均衡优化搜索最优解的性能.之后,将提出的均衡优化集成极限学习机对电力系统的客户进行分类.通过真实数据集上的实验表明,在不同的分类指标下,所提出的均衡优化集成极限学习机都具有良好的预测效果,可为电力系统客户管理与服务提供有效的技术手段.
Accurate classification of power system customers can enable differentiated management and personalized services for customers. In order to address the challenges associated with accurate customer classification, this paper proposes a classification method based on an equilibrium optimizer and an extreme learning machine. In this method, an adaptive competition mechanism is proposed to balance the global exploration and local mining ability of an equilibrium optimizer, improving the performance of algorithms in finding optimal solutions. Thereafter, the proposed equilibrium optimizer is integrated with an extreme learning machine to classify the customers of a power system. Experiments on real data sets showed that the proposed algorithm integrated with an extreme learning machine offers more accurate performance for different classification indexes;hence, the proposed method can provide an effective technical means for power system customer management and service.
作者
郑思达
刘岩
杨晓坤
戚成飞
袁培森
ZHENG Sida;LIU Yan;YANG Xiaokun;QI Chengfei;YUAN Peisen(Metrology Center,State Grid Jibei Electric Power Supply Company,Beijing 100045 China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210095,China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第5期146-156,共11页
Journal of East China Normal University(Natural Science)
关键词
均衡优化
极限学习机
电力系统
客户分类
equilibrium optimizer
extreme learning machine
power system
customer classification
