中国电力 ›› 2022, Vol. 55 ›› Issue (9): 140-145.DOI: 10.11930/j.issn.1004-9649.202105059

• 电网 • 上一篇    下一篇

基于综合能源计量数据的区域用能特性分析

王新刚, 赵舫, 朱文君   

  1. 国网上海市电力公司电力科学研究院,上海 200437
  • 收稿日期:2021-05-14 修回日期:2022-08-04 发布日期:2022-09-20
  • 作者简介:王新刚(1982—),男,通信作者,硕士,高级工程师,从事电力大数据分析研究,E-mail:wxgang2328@163.com;赵舫(1989—),女,硕士,工程师,从事用电信息采集研究,E-mail:zhaofangzhaofang@sina.com;朱文君(1994—),女,工程师,从事电能计量与电力大数据分析研究,E-mail:zhuwenjun@163.com
  • 基金资助:
    国家电网有限公司科技项目(52094019000Z)

Pattern Analysis of Regional Energy Consumption Based on Integrated Energy Measurement Data

WANG Xingang, ZHAO Fang, ZHU Wenjun   

  1. State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China
  • Received:2021-05-14 Revised:2022-08-04 Published:2022-09-20
  • Supported by:
    This work is supported by Science and Technology Project of SGCC (No. 52094019000Z)

摘要: 用户耗能的时空分布特性对于电网规划具有重要的参考价值,是电网建设的重要依据。电力运营商通过新型计量设备分析居民的用电行为,从而促进电网高效运营。针对区域用户画像问题,提出基于综合能源计量数据的区域用能特性分析方法。利用“多表合一”用能采集系统,引入用户的燃气消耗数据,基于层次聚类与自组织映射(self organized maps,SOM)聚类方法刻画用能特征分布,通过仿真实验说明所提方法在用能特性分析问题上具有实用价值,有利于挖掘高耗能区域,能帮助电力运营商制定规划方案。

关键词: 用能特性, 模式分析, 多表合一, 聚类方法

Abstract: The spatio-temporal pattern of users’ energy consumption can offer an important guideline for power system planning, and it is also one of the most important basis for power grid construction. Through widely deployed new-type advanced metering infrastructures, power grid operators are able to analyze the users’ energy consumption patterns, which can promote the efficient operation of power grids. Towards regional user profile for energy consumption, a regional energy consumption pattern analysis approach is proposed based on integrated energy measurement data. Using multi-meter integration energy-consumption acquisition system, the gas consumption data is introduced for analysis. The hierarchical clustering and self-organized maps (SOM) methods are used to describe the energy consumption patterns. Simulation and experimental results show that the proposed method is of practical use in energy consumption analysis and conducive to discovering high energy-consumption regions, which is helpful for power grid operators to formulate power system planning schemes.

Key words: energy consumption pattern, pattern analysis, multi-meter integration, clustering algorithm