企业电力征信大数据价值挖掘与应用

辛保江; 李德文; 王兰兰

doi:10.11959/j.issn.2096-0271.2021067

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企业电力征信大数据价值挖掘与应用

应用 | 更新时间：2024-06-03

- 企业电力征信大数据价值挖掘与应用
- Value mining and application of big data in enterprise power credit investigation
- 大数据 2021年7卷第6期页码：138-146
- 作者机构：
- 作者简介：
  
  [ "辛保江（1986- ），男，国网山东省电力公司潍坊供电公司副高级工程师，主要研究方向为电力大数据价值挖掘与应用" ]
  [ "李德文（1994- ），男，国网山东省电力公司潍坊供电公司助理工程师，主要研究方向为电力大数据应用" ]
  [ "王兰兰（1986- ），女，国网山东省电力公司潍坊供电公司经济师，主要研究方向为电力营销服务" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021067
  中图分类号： U495
- 网络首发：2021-11，
  
  纸质出版：2021-11-15
- 稿件说明：
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辛保江, 李德文, 王兰兰. 企业电力征信大数据价值挖掘与应用[J]. 大数据, 2021,7(6):138-146.

Baojiang XIN, Dewen LI, Lanlan WANG. Value mining and application of big data in enterprise power credit investigation[J]. Big data research, 2021, 7(6): 138-146.
辛保江, 李德文, 王兰兰. 企业电力征信大数据价值挖掘与应用[J]. 大数据, 2021,7(6):138-146. DOI： 10.11959/j.issn.2096-0271.2021067.

Baojiang XIN, Dewen LI, Lanlan WANG. Value mining and application of big data in enterprise power credit investigation[J]. Big data research, 2021, 7(6): 138-146. DOI： 10.11959/j.issn.2096-0271.2021067.

摘要

针对传统电力征信平台稳定性不足、测试准确性低等缺点，研究设计了一个电力征信大数据平台。使用联机分析法对电力大数据进行分析，并将其分为用户行为、费用细则、用户价值与个人信用四大类。以模块化结构为基础，分别对数据采集模块、数据分析模块、用户交互模块进行优化设计，采用KNN算法和交叉验证法对用电数据进行分类与决策处理，得出区域的用电规律，以此设计和调整配电方案。最后将提出的平台与传统电力征信平台进行对比，实验结果表明，提出的平台的稳定性和准确性都有所提升，在测试过程中准确性高达98.9%。

Abstract

Aiming at the shortcomings of traditional power credit investigation platform such as insufficient stability and low test accuracy

a big data power credit investigation platform was designed.The online analytical method was used to analyze power big data

which was divided into four categories: user behavior

expense rules

user value and personal credit.Based on the modular structure

the data acquisition module

data analysis module and user interaction module were optimized respectively.The KNN and cross validation method were used to classify and process the power consumption data

and the regional power consumption law was obtained

so as to design and adjust the distribution scheme.Finally

the platform was compared with the traditional power credit investigation platform.The experimental results show that the stability and accuracy of the platform are improved

and the accuracy is as high as 98.9% during the test.

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references

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