A Dynamic Clustering Method Considering User Behavior and Quantity Evolution

MAO Zhihang; SUN Yingyun; GAO Yi; ZHANG Liang; MA Weigang

doi:10.19725/j.cnki.1007-2322.2023.0364

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

A Dynamic Clustering Method Considering User Behavior and Quantity Evolution

更新时间：2025-12-11

- A Dynamic Clustering Method Considering User Behavior and Quantity Evolution
- Issue 6, Pages: 1193-1201(2025)
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院, 北京市昌平区,102206
  2. 国网天津市电力公司经济技术研究院, 天津市河东区,300171
  3. 国网天津静海供电有限公司, 天津市静海区,301600
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0364
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
MAO Zhihang, SUN Yingyun, GAO Yi, et al. A Dynamic Clustering Method Considering User Behavior and Quantity Evolution[J]. 2025, (6): 1193-1201.
DOI：

MAO Zhihang, SUN Yingyun, GAO Yi, et al. A Dynamic Clustering Method Considering User Behavior and Quantity Evolution[J]. 2025, (6): 1193-1201. DOI： 10.19725/j.cnki.1007-2322.2023.0364.

摘要

作为新型电力系统“源荷互动”的重要手段之一，负荷聚合服务可将海量用户负荷资源有序分群，有助于提升对海量负荷的调控能力。现有用户分群方法多基于聚类方法，过度依赖于用户的历史静态特征，忽略了用户未来数量的持续变化和用电行为演变等问题，不利于用户用电规律的挖掘。针对该问题，通过分析用户用电行为与数量演变问题对用户分群结果及建模的不利影响，提出一种基于进化聚类的用户动态分群算法，保证了用户分群结果在各时间步的一致性与合理性。采用多个真实数据集验证所提方法的有效性，仿真分析结果表明，所提算法可以实现合理、平稳且灵活的用户分群方式，且有助于集群负荷预测精度的提升。

Abstract

As one of the important means of “source-load interaction” in the new power system

load aggregation service can help to improve the regulation and control capability of massive loads by orderly clustering of massive user load resources. Existing user clustering methods are mostly based on clustering methods

which are overly dependent on the historical static characteristics of users

ignoring the continuous changes in the number of users in the future and the evolution of electricity consumption behavior

which is not conducive to the mining of the laws of user electricity consumption. To address this problem

this paper analyzes the adverse effects of the evolution of user behavior and quantity on user clustering results and modeling

and proposes a user dynamic clustering algorithm based on the evolutionary clustering

which ensures the consistency and reasonableness of the user clustering results in each time step. The validity of the proposed method is verified by using several real data sets

and the simulation analysis results show that the proposed algorithm can realize a reasonable

smooth and flexible user clustering method

and help to improve the accuracy of cluster load forecasting.

关键词

Keywords

references

DONG Lei, CHEN Zhenping, HAN Fujia, et al. Short-term load forecasting of residential user groups based on graph convolutional neural network and K-means clustering[J]. Power System Technology, 2023, 47(10): 4291−4301(in Chinese).

董雷, 陈振平, 韩富佳, 等. 基于图卷积神经网络与K-means聚类的居民用户集群短期负荷预测[J]. 电网技术, 2023, 47(10): 4291−4301.

LI Mingxuan, QI Buyang, HE Dawei. Optimization of aggregation of demand response resources in industrial park[J]. Power System Technology, 2022, 46(9): 3543−3549(in Chinese).

李明轩, 齐步洋, 贺大玮. 工业园区需求响应资源聚合优化配置方法[J]. 电网技术, 2022, 46(9): 3543−3549.

LI Jinghua, XIE Yutian, ZENG Hongyu, et al. Research review of uncertain optimal scheduling and its application in new-type power systems[J]. High Voltage Engineering, 2022, 48(9): 3447−3464(in Chinese).

黎静华, 谢育天, 曾鸿宇, 等. 不确定优化调度研究综述及其在新型电力系统中的应用探讨[J]. 高电压技术, 2022, 48(9): 3447−3464.

YU C N, MIROWSKI P, HO T K. A sparse coding approach to household electricity demand forecasting in smart grids[J]. IEEE Transactions on Smart Grid, 2017, 8(2): 738−748.

ZHANG Huibo, WANG Shouxiang, ZHAO Qianyu, et al. Residential user load curve classification method considering data imbalance[J]. Power Engineering Technology, 2022, 41(3): 186−193(in Chinese).

张慧波, 王守相, 赵倩宇, 等. 考虑数据不均衡的居民用户负荷曲线分类方法[J]. 电力工程技术, 2022, 41(3): 186−193.

ZHU Tianyi, AI Qian, HE Xing, et al. Review on data-driven methods and applications of electricity consumption behavior analysis[J]. Electric Power Engineering Technology, 2020, 44(9): 3497−3507(in Chinese).

朱天怡, 艾芊, 贺兴, 等. 基于数据驱动的用电行为分析方法及应用综述[J]. 电网技术, 2020, 44(9): 3497−3507.

AL-OTAIBI R, JIN N, WILCOX T, et al. Feature construction and calibration for clustering daily load curves from smart-meter data[J]. IEEE Transactions on Industrial Informatics, 2016, 12(2): 645−654.

CHEN Ye, WU Hao, SHI Junyi, et al. Application of singular value decomposition for dimensionality reduction and clustering analysis of daily load curves[J]. Automation of Electric Power Systems, 2018, 42(3): 105−111(in Chinese).

陈烨, 吴浩, 史俊祎, 等. 奇异值分解方法在日负荷曲线降维聚类分析中的应用[J]. 电力系统自动化, 2018, 42(3): 105−111.

WANG Y, CHEN Q X, KANG C Q, et al. Clustering of electricity consumption behavior dynamics toward big data applications[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2437−2447.

CHÉVEZ P, BARBERO D, MARTINI I, et al. Application of the K-means clustering method for the detection and analysis of areas of homogeneous residential electricity consumption at the Great La Plata region, Buenos Aires, Argentina[J]. Sustainable Cities and Society, 2017, 32: 115−129.

ZHANG Y, LIU Y, YU Z, et al. Improving aggregated load forecasting using evidence accumulation k-shape clustering[C]//2020 IEEE Power Energy Society General Meeting (PESGM). Montreal, QC, Canada, 2020: 1−5.

LIU Yang, LIU Yang, XU Lixiong. High-performance back propagation neural network algorithm for classification of mass load data[J]. Automation of Electric Power Systems, 2018, 42(21): 131−140(in Chinese).

刘洋, 刘洋, 许立雄. 适用于海量负荷数据分类的高性能反向传播神经网络算法[J]. 电力系统自动化, 2018, 42(21): 131−140.

YANG J J, ZHAO J H, WEN F S, et al. A model of customizing electricity retail prices based on load profile clustering analysis[J]. IEEE Transactions on Smart Grid, 2019, 10(3): 3374−3386.

LI Yan, JIA Yajun, LI Lei, et al. Short-term power load forecasting based on random forest algorithm[J]. Power System Protection and Control, 2020, 48(21): 117−124(in Chinese).

李焱, 贾雅君, 李磊, 等. 基于随机森林算法的短期电力负荷预测[J]. 电力系统保护与控制, 2020, 48(21): 117−124.

XU K S, KLIGER M, HERO A O. Evolutionary spectral clustering with adaptive forgetting factor[C]//2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, TX, USA, 2010: 2174−2177.

SHENG Wanxing, JI Yu, WU Ming, et al. Dynamic clustering modeling of regional centralized photovoltaic power plant based on improved fuzzy C-means clustering algorithm[J]. Power System Technology, 2017, 41(10): 3284−3291(in Chinese).

盛万兴, 季宇, 吴鸣, 等. 基于改进模糊C均值聚类算法的区域集中式光伏发电系统动态分群建模[J]. 电网技术, 2017, 41(10): 3284−3291.

PHAM D T, DIMOV S S, NGUYEN C. An incremental K-means algorithm[J]. Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2004, 218(7): 783−795.

LANGFORD J, ZHANG X, BROWN G, et al. Evolutionary clustering[M]. Sammut C, Webb G I, eds. Encyclopedia of Machine Learning. Boston: Springer US, 2011: 332−337.

NIU Xinzheng, SI Weiyu, SHE Kun. Dynamic network association discovery based on evolutionary clustering[J]. Journal of Software, 2017, 28(7): 1773−1789(in Chinese).

牛新征, 司伟钰, 佘堃. 基于进化聚类的动态网络社团发现[J]. 软件学报, 2017, 28(7): 1773−1789.

FOLINO F, PIZZUTI C. An evolutionary multiobjective approach for community discovery in dynamic networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1838−1852.

KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 841−851.

SHENSA M J. The discrete wavelet transform: wedding the a trous and Mallat algorithms[J]. IEEE Transactions on Signal Processing, 1992, 40(10): 2464−2482.

HABEN S, SINGLETON C, GRINDROD P. Analysis and clustering of residential customers energy behavioral demand using smart meter data[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 136−144.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Decision Model of Day Ahead Frequency Regulation for Electric Vehicle Users Based on Prospect Theory

A Simulating Study on Superheated Steam Temperature Control System of a Power Plant Based on NN & IMC

Research on Decoupling of the Coordinated Control System in a 300MW Thermal Power Unit

Resarch on Voltage and Reactive Power Control forSubstations Based on fuzzy Neural Networks

A Study on Integrated Intelligent System for Fault Diagnosis

Related Author

Haotian WANG

Yingyun SUN

Liwei WANG

Xu Hongbing 2

Li Xiaolong 1

Li Nongzhuang 1

Hou Guolian 1

Zhang Jianhua 1

Related Institution

School of Electrical and Electronic Engineering, North China Electric Power University,Changping District

1 North China Universty of Electric Power

河北保定电校!保定071000

Dept. of Thermal Power Engineering, NCEPU

North China Electric Power University

AI问答

⁰