基于周期调整负荷成分指数的行业用电大数据价值挖掘
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TM-9

基金项目:

深圳供电局有限公司科技项目(SZKJXM20190594)


Big data mining of industry power consumption based on component index about seasonal-adjusted load
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    能源数字经济背景下为展示行业用户的用电行为,探索地区和行业的经济景气情况,本文借鉴股市成分指数提出一种周期调整负荷成分指数。首先,按照给定规则筛选出具有代表性的若干位行业用户;然后,利用周期分解算法提取所选用户历史日电量的周期分量,以计算调整日电量,并提出工作强度系数,再考虑其所涉及的行业及其个体差异提出多项指标,并基于模糊专家评价法等方式计算其权重以对调整日电量加权整合;最后,选定基期展示负荷指数曲线。分析可得,工作强度系数有助于联系实际生产活动,指数能够及时反映地区日用电行为模式,且去除气温影响后与经济指标强相关,能够阐释投资、产出与生产间的关系以及表征社会主体经济的动向。

    Abstract:

    Under the background of energy digital economy, in order to display and analyze power consuming behavior, and to explore regional economic trends, this paper proposes a periodic adjusted load component index by referring to the stock market index. Firstly, some representative enterprise users are selected as samples based on given rules. Then, the periodic components of the selected user's historical daily electricity quantity are extracted by STL. Hence, the adjustment of the daily electricity quantity can be calculated, and the working strength coefficient is proposed. Then, according to the industry and individual differences, multiple indices are proposed, and the cycle adjusted daily load is weighted and integrated by fuzzy expert evaluation method. After that, based on a selected day’s value, the load trend can be displayed. Finally, the analysis shows that the working strength coefficient is helpful to link with the actual production activities, and the load index can reflect regional daily electricity consumption behavior. Furthermore, after neglecting the influence of temperature, the index has a strong correlation with economic indicators, which can explain the relationship between investment, output and production, and represent the economy of social subject.

    参考文献
    相似文献
    引证文献
引用本文

严玉婷,薛冰,方力谦,等.基于周期调整负荷成分指数的行业用电大数据价值挖掘[J].电力科学与技术学报,2022,37(6):181-189.
YAN Yuting, XUE Bing, FANG Liqian, et al. Big data mining of industry power consumption based on component index about seasonal-adjusted load[J]. Journal of Electric Power Science and Technology,2022,37(6):181-189.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2023-01-16
  • 出版日期: