基于动态层次 K － Modes 的电网数据聚类分析

Clustering Analysis of Power Grid Data Based on Moving Hierarchical K － modes Method

摘要:

随着电力工业的发展与电力计量体系的不断完善，电网需求侧用电特性呈多样化发展态势。智能电表走进 人们的生活，带来海量电力数据。如何挖掘用户的用电行为特性，从而促进电价市场化成为人们所关心的问题。首先 介绍了聚类分析中的 K － modes 算法以及层次 K － means 算法，并结合考虑其优缺点提出动态层次 K － modes 算法来 处理类属型数据并给出合理的 k 值; 其次提出了将曲线数据进行差分及类属型转化的数据处理方法，使之能更好地反 应用户曲线形态; 最后利用动态层次 K － modes 算法在模拟数据以及厦门岛内地区电力用户的真实数据上进行聚类 试验，得到优良的分类结果。

Abstract:

Withing the development of power industry and the continuous improvement of electric power metering system，the features of power grid demand side present a diversified development． Smart meters come into people＇s life，which is bringing huge amounts of electricity data． A meaningful problem is how to explore the behavior of users to promote the electricity market． Combining the considerations of traditional K － modes method and hierarchical K － means method，moving hierarchical K － modes method is proposed，which is able to deal with categorical data and gives a reasonable k value of the number of clusters． Moreover，a method to transform curve data into categorical data by differencing and categorizing is proposed，which can preferably reflect the shape of curves． At last，clusetering experiment is carried out by using moving hierarchical K － modes method based on simulated data and real data of power users in Xiamen island region，which gets excellent clustering results．