湖南电力 ›› 2024, Vol. 44 ›› Issue (4): 73-83.doi: 10.3969/j.issn.1008-0198.2024.04.011

• 新技术及应用 • 上一篇    下一篇

基于VAE-Kmeans算法的台区聚类精准画像技术

于宗超1,2, 陈孜孜1,2, 文明1,2, 罗姝晨1,2, 韦东3, 辛立杰3   

  1. 1.国网湖南省电力有限公司经济技术研究院,湖南 长沙 410007;
    2.能源互联网供需运营湖南省重点实验室,湖南 长沙 410007;
    3.北京清能互联科技有限公司, 北京 100000
  • 收稿日期:2024-03-18 修回日期:2024-04-01 出版日期:2024-08-25 发布日期:2024-09-09
  • 通信作者: 于宗超(1996),男,博士,工程师,主要研究方向为负荷预测、能源电力发展、供需平衡、新能源消纳、电网优化运行。
  • 基金资助:
    国网湖南省电力有限公司科技项目(5216A2220014);湖南省科技创新平台与人才计划(2019TP1053)

Precision Profiling Technology of Transformer Areas Clustering Based on VAE-Kmeans Algorithm

YU Zongchao1,2, CHEN Zizi1,2, WEN Ming1,2, LUO Shuchen1,2, WEI Dong3, XIN Lijie3   

  1. 1. State Grid Hunan Electric Power Company Limited Economic and Technological Research Institute, Changsha 410007, China;
    2. Hunan Key Laboratory of Energy Internet Supply-Demand and Operation, Changsha 410007, China;
    3. Beijing Tsintergy Technology Co., Ltd., Beijing 100000, China
  • Received:2024-03-18 Revised:2024-04-01 Online:2024-08-25 Published:2024-09-09

摘要: 构建起台区用电的精准画像,刻画台区负荷特性及用电模式,对精细化负荷预测、负荷波动分析溯源及台区业务场景能够起到精准指导作用。为此,提出一种基于VAE(变分自编码)-Kmeans算法的台区聚类精准画像技术。采用变分自编码、Kmeans聚类和时间序列相似度算法,从多种维度提取台区和行业负荷的典型曲线,通过综合评估确定最相似的行业负荷,为台区分配相应的画像标签。以占比较大的居民台区为例,构建基于聚类分析和决策树算法的深度挖掘和精准描绘策略,形成了14种城乡居民用电台区精准画像类别,为精细化负荷预测、差异化台区运维管理策略提供了坚实基础。

关键词: 台区精准画像, 典型曲线, 聚类分析, 负荷曲线

Abstract: Building a precise portrait of electricity consumption in the substation area, depicting the load characteristics and electricity consumption patterns of the substation area, can provide precise guidance for refined load forecasting, load fluctuation analysis and tracing, and substation business scenarios. Therefore, this paper proposes a precision profiling technology of transformer areas clustering based on VAE(variational auto-encoder)-Kmeans algorithm. Firstly,VAE, Kmeans clustering and time series similarity algorithms are used to extract the typical curves of load for station areas and industries from multiple dimensions. Secondly, the most similar industry load is determined through comprehensive evaluation, and the corresponding portrait label is assigned to the transformer area. Finally, taking the relatively large residential transformer area as an example, a deep mining and accurate description strategy based on cluster analysis and decision tree algorithm are constructed, and 14 sets of accurate portrait categories of urban and rural residential transformer areas are formed, which provides a solid foundation for fine load forecasting and differentiated transformer operation and maintenance management strategies.

Key words: precision portrait in the substation area, typical curve, cluster analysis, load curve

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