Application of big data method in forecasting the risk of tariff recovery

Yadi ZHAO; Zhao WU; Qingbing LI; Xiaofeng CHEN; Baoting WANG

doi:10.11959/j.issn.1000-0801.2019040

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Application of big data method in forecasting the risk of tariff recovery

Power Informatization Column | 更新时间：2024-06-05

- Application of big data method in forecasting the risk of tariff recovery
- Telecommunications Science Vol. 35, Issue 2, Pages: 125-133(2019)
- 作者机构：
  
  1. 北京国网信通埃森哲信息技术有限公司，北京 100032
  2. 国网信息通信产业集团有限公司，北京 102211
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019040
  CLC： O213.1
- Published Online：2019-02，
  
  Published：20 February 2019
- 稿件说明：
移动端阅览
Yadi ZHAO, Zhao WU, Qingbing LI, et al. Application of big data method in forecasting the risk of tariff recovery[J]. Telecommunications science, 2019, 35(2): 125-133.
DOI：

Yadi ZHAO, Zhao WU, Qingbing LI, et al. Application of big data method in forecasting the risk of tariff recovery[J]. Telecommunications science, 2019, 35(2): 125-133. DOI： 10.11959/j.issn.1000-0801.2019040.

摘要

基于电力客户的历史数据，采用客户的基本属性、用电行为、缴费行为、客户信用、行业前景信息等多个维度确定模型所需指标体系。通过指标的相关系数矩阵及信息值（information value，IV）筛选出最终进入模型的指标变量，同时采用最优分组的方法对变量进行分组，并进行证据权重转化（weight of evidence， WOE）。基于处理后的数据，运用逻辑回归算法构建用电客户电费风险预测模型，并依据得到的模型结果量化输出变量标准评分卡表，从而将客户划分为高风险、中风险和低风险用户，为不同的用户采取差异化的营销措施提供依据。

Abstract

Based on the historical data of electricity customers

the model index system was determined according to the customers’ basic attributes

the electricity consumption and the payment behavior

the customers’ credit

the industry prospects’ information and so on.Through the correlation coefficient matrix and the information value of the index

the index variables that enter the model were selected.At the same time

the best grouping method was used to group variables and WOE (weight of evidence) transformation was carried out.Based on the processed data

the logic regression algorithm were used to construct the electricity cost risk forecasting model of the electric customers

and output variable standard score card was quantified according to the model results.Thus the customers were divided into high

middle and low risk users that could provide the basis for taking differential marketing measures to the different customers.

关键词

Keywords

references

ROBU V , VINYALS M , ROGERS A , et al . Efficient buyer groups for prediction-of-use electricity tariffs [C ] // AAAI Conference on Artificial Intelligence,July 27-31,2014,Québec City,Québec,Canada . Palo Alto:AIII Press , 2014 : 451 - 457 .

PEPERMANS G , WILLEMS B . Cost recovery in congested electricity networks [J ] . Zeitschrift Für Energiewirtschaft , 2010 , 34 ( 3 ): 195 - 208 .

张晓峰 . 电力大客户电费回收风险防范体系的构建 [J ] . 内蒙古科技与经济 , 2013 ( 24 ): 121 - 123 .

ZHANG X F . Construction of risk prevention system for electric power customer's electricity recovery [J ] . Inner Mongolia Science Technology ＆ Economy , 2013 ( 24 ): 121 - 123 .

裘华东 , 涂莹 , 丁麒 . 基于标签库系统的电力企业客户画像构建与信用评估及电费风险防控应用 [J ] . 电信科学 , 2017 , 33 ( 1 ): 214 - 221 .

QIU H D , TU Y , DING Q . Construction of power customer portrait and its credit evaluation and electricity fee risk control based on tag library system [J ] . Telecommunications Science , 2017 , 33 ( Z1 ): 214 - 221 .

HUANG Y , YAN Y . Research of evaluating credit-risk in power enterprise based on SVM and VIKOR method [C ] // IEEE International Conference on Industrial Engineering and Engineering Management,Dec 8-11,2009,Singapore . Piscataway:IEEE Press , 2009 : 1596 - 1599 .

刘金 . 基于改进层次分析法的电力客户欠费风险评价 [J ] . 电子世界 , 2014 ( 19 ): 182 - 183 .

LIU J . Risk evaluation of electric power customer arrears based on improved analytic hierarchy process [J ] . Electronic World , 2014 ( 19 ): 182 - 183 .

WIGAN M R , CLARKE R . Big data’s big unintended consequences [J ] . Computer , 2013 , 46 ( 6 ): 46 - 53 .

RAHMAN M N , ESMAILPOUR A , ZHAO J . Machine learning with big data an efficient electricity generation forecasting system [J ] . Big Data Research , 2016 ( 5 ): 9 - 15 .

杨华飞 , 李栋华 , 程明 . 电力大数据关键技术及建设思路的分析和研究 [J ] . 电力信息与通信技术 , 2015 , 13 ( 1 ): 7 - 10 .

YANG H F , LI D H , CHENG M . Analysis and research of key technologies and construction ideas of power big data [J ] . Electric Power Information and Communication Technology , 2015 , 13 ( 1 ): 7 - 10 .

赵永良 , 秦萱 , 吴尚远 , 等 . 基于数据挖掘的高压用户电费回收风险预测 [J ] . 电力信息与通信技术 , 2015 , 13 ( 9 ): 57 - 61 .

ZHAO Y L , QIN X , WU S Y , et al . Electricity recovery risk prediction of high-voltage customers based on data mining [J ] . Electric Power Information and Communication Technology , 2015 , 13 ( 9 ): 57 - 61 .

黄文思 , 郝悍勇 , 李金湖 , 等 . 基于决策树算法的电力客户欠费风险预测 [J ] . 电力信息与通信技术 , 2016 ( 1 ): 19 - 22 .

HUANG W S , HAO H Y , LI J H , et al . Prediction of power customer arrear risk based on decision tree algorithm [J ] . Electric Power Information and Communication Technology , 2016 ( 1 ): 19 - 22 .

吴漾 , 朱州 . 基于特征选择改进LR-Bagging算法的电力欠费风险居民客户预测 [J ] . 电子产品世界 , 2017 ( 4 ): 70 - 75 .

WU Y , ZHU Z . The arrears risk prediction of power residential customers based on LR-Bagging algorithm improved by feature selection [J ] . Electronic Engineering and Products World , 2017 ( 4 ): 70 - 75 .

张禄 , 潘鸣宇 , 田贺平 , 等 . 基于数据挖掘技术的电力客户欠费风险预警研究 [J ] . 2017 .

ZHANG L , PAN M Y , TIAN H P , et al . Risk early-warning of electricity customers’arrears based on data mining technology [J ] . 2017 .

GRANELL R , AXON C J , WALLOM D C H . Predicting winning and losing businesses when changing electricity tariffs [J ] . Applied Energy , 2014 , 133 ( 10 ): 298 - 307 .

KAMILARIS A , KALLURI B , KONDEPUDI S , et al . A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings [J ] . Renewable ＆Sustainable Energy Reviews , 2014 ( 34 ): 536 - 550 .

GRANELL R , AXON C J , WALLOM D C H , et al . Power-use profile analysis of non-domestic consumers for electricity tariff switching [J ] . Energy Efficiency , 2016 , 9 ( 3 ): 825 - 841 .

FELDMAN R , SANGER J . Text mining handbook:advanced approaches in analyzing unstructured data [M ] . Oxford City : Cambridge University PressPress , 2006 .

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