COVID-19疫情环境下用电行为分析
作者:

Analysis of Electricity Consumption Behavior under COVID-19
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [19]
  • |
  • 相似文献 [20]
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    针对COVID-19这一特殊时期, 利用大数据技术, 处理原始不同数据结构的电力数据. 将用户用电行为的数据分成内部数据和外部数据, 其次是确定用电行为分析, 通过对传统的K-means聚类算法改进, 提高K-means效率. 最后利用改进算法聚类出的类别构建用电行为模型, 实现用户用电行为分析. 最终实现达到帮助国家电网公司达到电力智能分配的目的, 并且给出了大致的政策倾向. 提升国家各部门监管能力, 助力国家应急管理.

    Abstract:

    Aiming at special situation under COVID-19 epidemic environment, big data technology is used to process the power data of different data structures at the very beginning. Firstly, the data of users’ electrical behavior is divided into internal data and external data. Secondly, the behavior of electricity consumption is analyzed by improving K-means clustering algorithm, which can improve the efficiency of the traditional K-means algorithm. Finally, the improved K-means algorithm clustering is used to build the power consumption behavior model, so as to realize the results of the users’ power consumption behavior analysis model. This study helps the state grid corporation of China to achieve the purpose of intelligent distribution of electricity, and gives the general policy tendency, which improves the regulatory capacity of all government departments to help manage national emergencies.

    参考文献
    [1] 何永秀, 王冰, 熊威, 等. 基于模糊综合评价的居民智能用电行为分析与互动机制设计. 电网技术, 2012, 36(10): 247–252
    [2] 赵凯, 陈丽娟, 吴玉鹏, 等. 2004年全国电力可靠性统计分析. 中国电力, 2005, 38(5): 1–8. [doi: 10.3969/j.issn.1004-9649.2005.05.001
    [3] 肖乃慎, 李博, 孔德诗. 大数据背景下的电网客户用电行为分析系统设计. 电子设计工程, 2016, 24(17): 61–63, 69. [doi: 10.3969/j.issn.1674-6236.2016.17.019
    [4] Lu J, Zhu YP, Peng WH, et al. Feature selection strategy for electricity consumption behavior analysis in smart grid. Automation of Electric Power Systems, 2017, 41(5): 58–63, 83
    [5] Gong GJ, Chen ZM, Lu J, et al. Clustering optimization strategy for electricity consumption behavior analysis in smart grid. Automation of Electric Power Systems, 2018, 42(2): 58–63
    [6] Li Y, Luo Q, Song YQ, et al. Study on the tier tap determining of basic residential electricity consumption based on demand response. 2012 Asia-Pacific Power and Energy Engineering Conference. Shanghai, China. 2012. 1687–1692.
    [7] Mavroeidis D, Marchiori E. A novel stability based feature selection framework for k-means clustering. Proceedings of the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases. Athens, Greece. 2011. 421–436.
    [8] De Assunção MD, Orgerie AC, Lefevre L. An analysis of power consumption logs from a monitored grid site. Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing. Hangzhou, China. 2011. 61–68.
    [9] Abreu JM, Pereira FC, Ferrao P. Using pattern recognition to identify habitual behavior in residential electricity consumption. Energy and Buildings, 2012, 49: 479–487. [doi: 10.1016/j.enbuild.2012.02.044
    [10] 王炳鑫, 侯岩, 方红旺, 等. 面向削峰填谷的电力客户用电行为分析. 2016电力行业信息化年会论文集. 天津, 中国. 2016. 103–107.
    [11] 王炳鑫, 侯岩, 方红旺, 等. 面向“削峰填谷”的电力客户用电行为分析. 电信科学, 2017, 33(5): 164–170
    [12] 刘茵, 王立涛, 张晓飞, 等. 基于MATLAB仿真的用户用电行为分析及互动模式识别. 2016电力行业信息化年会论文集. 天津, 中国. 2016. 118–120.
    [13] 张小龙. 大数据环境下用户用电行为分析的研究[硕士学位论文]. 北京: 华北电力大学(北京), 2017.
    [14] 凌德祥, 黄拓, 关晓林, 等. 基于大数据的电力客户行为分析体系研究及实践. 电力大数据, 2018, 21(10): 13–17
    [15] 李志海, 张春平, 王子壬, 等. 结合PCA的K-means算法在专变用户用电行为分析中的应用. 电力信息与通信技术, 2018, 16(12): 62–67
    [16] 张灿. 基于用电行为分析的异常用电检测[硕士学位论文]. 武汉: 华中科技大学, 2018.
    [17] 蒋菱, 王旭东, 于建成, 等. 基于分布式计算的海量用电数据分析技术研究. 计算机技术与发展, 2016, 26(12): 176–181
    [18] 胡殿刚, 李韶瑜, 楼俏, 等. ELM算法在用户用电行为分析中的应用. 计算机系统应用, 2016, 25(8): 155–161. [doi: 10.15888/j.cnki.csa.005305
    [19] 郁启麟. K-means算法初始聚类中心选择的优化. 计算机系统应用, 2017, 26(5): 170–174. [doi: 10.15888/j.cnki.csa.005733
    引证文献
引用本文

关旭,王紫瑞,冀雯馨,郭一民. COVID-19疫情环境下用电行为分析.计算机系统应用,2021,30(1):282-287

复制
分享
文章指标
  • 点击次数:1067
  • 下载次数: 2336
  • HTML阅读次数: 1419
  • 引用次数: 0
历史
  • 收稿日期:2020-05-27
  • 最后修改日期:2020-06-23
  • 在线发布日期: 2020-12-31
文章二维码
您是第12436028位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号