考虑用户行为与数量演变的动态分群方法研究

毛志航; 孙英云; 高毅; 张梁; 马伟刚

doi:10.19725/j.cnki.1007-2322.2023.0364

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考虑用户行为与数量演变的动态分群方法研究

智能电网 | 更新时间：2025-12-11

- 考虑用户行为与数量演变的动态分群方法研究
- A Dynamic Clustering Method Considering User Behavior and Quantity Evolution
- 现代电力 2025年第6期页码：1193-1201
- 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：
  
  [ "毛志航(1998)，男，硕士研究生，研究方向为人工智能在电力系统中的应用，E-mail：zhihangmao@126.com" ]
  [ "孙英云(1975)，男，博士，教授，通信作者，研究方向为人工智能、优化理论在电力系统中的应用，E-mail：sunyy@ncepu.edu.cn" ]
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0364
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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毛志航, 孙英云, 高毅, 等. 考虑用户行为与数量演变的动态分群方法研究[J]. 现代电力, 2025,(6):1193-1201.

MAO Zhihang, SUN Yingyun, GAO Yi, et al. A Dynamic Clustering Method Considering User Behavior and Quantity Evolution[J]. 2025, (6): 1193-1201.
毛志航, 孙英云, 高毅, 等. 考虑用户行为与数量演变的动态分群方法研究[J]. 现代电力, 2025,(6):1193-1201. DOI： 10.19725/j.cnki.1007-2322.2023.0364.

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

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