首页> 中文期刊>计算机应用与软件 >基于聚类集成的用户负荷模式识别

基于聚类集成的用户负荷模式识别

     

摘要

In order to improve the reliability and universality of consumers’ load pattern recognition in peak shifting management, and aiming at the problem that the single clustering algorithm ( CA) is difficult to tackle the imbalance and timing characteristics of power load data, we present a cluster ensemble-based consumers’ load pattern recognition method.It uses multiple data standardisation methods and the selected CAs to generate diversified cluster members, and by combining all the cluster members to construct a consensus matrix and reconstructing it, it obtains a grouping result superior to the single CA’ s.This scheme is more robust and reliable than single CA in obtained clustering result of consumers’ power load data, and it also has the advantages of low sensitive to data structure changes, better clustering result and higher generalisation capability.The scheme achieves very good applied effect in power load pattern recognition in regard to 6 500 customers with special transformer in Zhongshan city.%为提高错峰管理中用户负荷模式识别的可靠性与普适性,针对目前单一聚类算法难以解决用电负荷数据的不平衡性以及时序特性等问题,提出一种基于聚类集成技术的用户负荷模型识别方案。利用多种标准化方法以及经遴选的聚类算法生成多样化的聚类成员,通过将所有聚类成员合并构造共识矩阵并进行重构,得到较单一聚类算法更为优越的分群结果。该方案比采用单一的聚类分析得到的用户用电负荷数据分簇结果更稳健可靠,且对数据结构变化的敏感度低、分簇效果更好、泛化能力更强,并在中山市6500家专变用户的用电负荷模式识别中取得了良好的应用效果。

著录项

相似文献

  • 中文文献
  • 外文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号