Early warning method for power supply service quality based on three-way decision theory and LSTM neural network
An efficient early-warning method for power supply service quality(PSSQ) is of great significance to optimize the customer experience of power service and ensure the security of power systems. Based on the power service data from the customer side, this paper proposes a early-warning method for PSSQ...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-08-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722004917 |
| _version_ | 1798001166282391552 |
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| author | Zhian Lin Yunchao Shi Bo Chen Shengyuan Liu Yuejun Ge Jien Ma Li Yang Zhenzhi Lin |
| author_facet | Zhian Lin Yunchao Shi Bo Chen Shengyuan Liu Yuejun Ge Jien Ma Li Yang Zhenzhi Lin |
| author_sort | Zhian Lin |
| collection | DOAJ |
| description | An efficient early-warning method for power supply service quality(PSSQ) is of great significance to optimize the customer experience of power service and ensure the security of power systems. Based on the power service data from the customer side, this paper proposes a early-warning method for PSSQ, which is based on three-way decision theory and long–short term memory (LSTM) network. First of all, four early-warning indicators (i.e., the proportion of complaints work orders indicator, the proportion of responsible work orders indicator, the proportion of duplicate work orders indicator and the average processing time indicator) of PSSQ are proposed according to requirements from the customer side. Secondly, the early-warning value of PSSQ is determined based on LSTM neural network and historical data. Then, the threshold of early warning decisions is determined based on the three-way decision theory. Finally, three local power supply companies in Zhejiang province are taken for case study to prove the effectiveness of the PSSQ early-warning method proposed. |
| first_indexed | 2024-04-11T11:31:48Z |
| format | Article |
| id | doaj.art-b9adbf4187a54ef1819a51e8be5529e0 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-11T11:31:48Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-b9adbf4187a54ef1819a51e8be5529e02022-12-22T04:26:07ZengElsevierEnergy Reports2352-48472022-08-018537543Early warning method for power supply service quality based on three-way decision theory and LSTM neural networkZhian Lin0Yunchao Shi1Bo Chen2Shengyuan Liu3Yuejun Ge4Jien Ma5Li Yang6Zhenzhi Lin7School of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Grid Zhejiang Marketing Service Center, Hangzhou 311122, ChinaState Grid Zhejiang Marketing Service Center, Hangzhou 311122, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaZhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding author.School of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; School of Electrical Engineering, Shandong University, Jinan 250061, ChinaAn efficient early-warning method for power supply service quality(PSSQ) is of great significance to optimize the customer experience of power service and ensure the security of power systems. Based on the power service data from the customer side, this paper proposes a early-warning method for PSSQ, which is based on three-way decision theory and long–short term memory (LSTM) network. First of all, four early-warning indicators (i.e., the proportion of complaints work orders indicator, the proportion of responsible work orders indicator, the proportion of duplicate work orders indicator and the average processing time indicator) of PSSQ are proposed according to requirements from the customer side. Secondly, the early-warning value of PSSQ is determined based on LSTM neural network and historical data. Then, the threshold of early warning decisions is determined based on the three-way decision theory. Finally, three local power supply companies in Zhejiang province are taken for case study to prove the effectiveness of the PSSQ early-warning method proposed.http://www.sciencedirect.com/science/article/pii/S2352484722004917Power supply service quality(PSSQ)Three-way decision theoryLong–short term memory (LSTM)Average decision cost |
| spellingShingle | Zhian Lin Yunchao Shi Bo Chen Shengyuan Liu Yuejun Ge Jien Ma Li Yang Zhenzhi Lin Early warning method for power supply service quality based on three-way decision theory and LSTM neural network Energy Reports Power supply service quality(PSSQ) Three-way decision theory Long–short term memory (LSTM) Average decision cost |
| title | Early warning method for power supply service quality based on three-way decision theory and LSTM neural network |
| title_full | Early warning method for power supply service quality based on three-way decision theory and LSTM neural network |
| title_fullStr | Early warning method for power supply service quality based on three-way decision theory and LSTM neural network |
| title_full_unstemmed | Early warning method for power supply service quality based on three-way decision theory and LSTM neural network |
| title_short | Early warning method for power supply service quality based on three-way decision theory and LSTM neural network |
| title_sort | early warning method for power supply service quality based on three way decision theory and lstm neural network |
| topic | Power supply service quality(PSSQ) Three-way decision theory Long–short term memory (LSTM) Average decision cost |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722004917 |
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