Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm

张凯; 冯剑; 刘建华; 白新雷; 宫飞翔; 刘祖东; 朱栋; 高赐威; 吴英俊

doi:10.3969/j.issn.1009-1831.2022.03.016

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Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm

更新时间：2026-03-05

- Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm
- Vol. 24, Issue 3, (2022)
- 作者机构：
  
  1. 国网河北省电力有限公司,石家庄,050000
  2. 中国电力科学研究院有限公司,北京,100192
  3. 河海大学能源与电气学院,南京,211100
  4. 东南大学电气工程学院,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.03.016
  CLC：
- Published：2022
- 稿件说明：
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张凯, 冯剑, 刘建华, et al. Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm[J]. 2022, 24(3).
DOI：

张凯, 冯剑, 刘建华, et al. Power consumption behavior pattern classification based on fuzzy C-mean clustering algorithm[J]. 2022, 24(3). DOI： 10.3969/j.issn.1009-1831.2022.03.016.

摘要

通过对负荷数据的归类分析，可以得到电力用户的用电行为特征，为需求响应策略制定和效果评估提供支撑。首先，对负荷数据进行预处理，包括非正常数识别与处理，以及平滑处理去除毛刺数据；其次，针对模糊C均值聚类算法对初始聚类中心敏感、易陷入局部最优和受噪声影响大等问题，提出采用最短距离法聚类为模糊 C 聚类提供初始聚类中心、利用有效性分析类内样本相似程度和不同类之间独立程度来判别聚类结果优劣、以及通过数据密度识别并剔除噪声点等改进措施，提升了模糊 C 均值聚类算法性能；最后，通过对比其他方法以及对某纺织企业负荷聚类分析，验证了改进算法的正确性与有效性。

Abstract

Through the classification and analysis of load data

the behavior characteristics of power users can be obtained

which provides support for the formulation of demand response strategies and effect evaluation. Firstly

the load data is preprocessed including identifying and processing abnormal numbers

and smoothing to remove burr data. Secondly

with the reason that the fuzzy C-means clustering algorithm is sensitive to the initial cluster center

easy to fall into local optimality

and is greatly affect-ed by noise

the shortest distance clustering method is proposed to provide the initial clustering center for fuzzy C clustering. The efficiency index is used to select the best clustering results of different categories

and the data density is used to identify and eliminate noise points.Finally

through comparison with other methods and cluster analysis of a certain hemp spinning enterprise’s load

the correctness and effectiveness of the improved algorithm is verified.

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