Application of big data method in forecasting the risk of tariff recovery
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...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019040/ |
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| _version_ | 1841530445076889600 |
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| author | Yadi ZHAO Zhao WU Qingbing LI Xiaofeng CHEN Baoting WANG |
| author_facet | Yadi ZHAO Zhao WU Qingbing LI Xiaofeng CHEN Baoting WANG |
| author_sort | Yadi ZHAO |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ddf2da2d3a849e7a7030da7e19ded80 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-5ddf2da2d3a849e7a7030da7e19ded802025-01-15T03:03:23ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-02-013512513359591258Application of big data method in forecasting the risk of tariff recoveryYadi ZHAOZhao WUQingbing LIXiaofeng CHENBaoting WANGBased 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019040/tariff recoverylogical regression algorithmindex system |
| spellingShingle | Yadi ZHAO Zhao WU Qingbing LI Xiaofeng CHEN Baoting WANG Application of big data method in forecasting the risk of tariff recovery Dianxin kexue tariff recovery logical regression algorithm index system |
| title | Application of big data method in forecasting the risk of tariff recovery |
| title_full | Application of big data method in forecasting the risk of tariff recovery |
| title_fullStr | Application of big data method in forecasting the risk of tariff recovery |
| title_full_unstemmed | Application of big data method in forecasting the risk of tariff recovery |
| title_short | Application of big data method in forecasting the risk of tariff recovery |
| title_sort | application of big data method in forecasting the risk of tariff recovery |
| topic | tariff recovery logical regression algorithm index system |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019040/ |
| work_keys_str_mv | AT yadizhao applicationofbigdatamethodinforecastingtheriskoftariffrecovery AT zhaowu applicationofbigdatamethodinforecastingtheriskoftariffrecovery AT qingbingli applicationofbigdatamethodinforecastingtheriskoftariffrecovery AT xiaofengchen applicationofbigdatamethodinforecastingtheriskoftariffrecovery AT baotingwang applicationofbigdatamethodinforecastingtheriskoftariffrecovery |