企业电力征信大数据价值挖掘与应用

doi:10.11959/j.issn.2096-0271.2021067

摘要/Abstract

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%。

关键词: 大数据模型, 综合型算法, 网络架构, 系统测试, 电力征信

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.

Key words: big data model, power credit investigation, comprehensive algorithm, network architecture, system testing

中图分类号:

U495

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.

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对比项	用电量预测值/万kW·h	用电量真实值/万kW·h	缴费预测值/万元	缴费真实值/万元	准确性
一号平台	7.0	5.2	4.2	3.51	74.2%
二号平台	5.6	5.2	4.0	3.51	85.3%
本文平台	5.0	5.2	3.49	3.51	98.9%