Time-sharing characteristic clustering analysis of household energy consumption via K-Mussels wandering optimization

被引：0

作者：

机构：

[1] An, Jing

[2] Liu, Shi Yao

[3] Kang, Qi

[4] 3,Yan, Wei

来源：

Yan, Wei | 1600年 / American Scientific Publishers卷 / 12期

关键词：

Electric utilities - Particle swarm optimization (PSO) - Data mining - Molluscs;

D O I：

10.1166/sl.2014.3271

中图分类号：

学科分类号：

摘要：

Clustering method based on swarm intelligence has high effectiveness. It plays an important role in quality data mining and various applications. This work presents a new clustering instance called K-MWO by combining K-means and mussels wandering optimization (MWO). The clustering analysis of the individual household electric power consumption data proves its application effectiveness, compared with the results of K-means and K-PSO. Through the application examples, the household electricity regulation can be analyzed to optimize the power consumption mode. Copyright © 2014 American Scientific Publishers.

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