| Autor: |
MuXin Zhang, XinRan Liu, Ying Shang, LiYan Kang, QiuTong Wu, WenYu Cheng |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Energy Reports, Vol 8, Iss , Pp 916-925 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-4847 |
| DOI: |
10.1016/j.egyr.2022.02.045 |
| Popis: |
For a long time, power supply enterprises have been plagued by the problem of electricity theft and default. Although power supply enterprises have strengthened anti-theft measures from various aspects, the huge benefits brought by electricity theft and default have driven the development trend of electricity theft methods to be more complicated and concealed, which has brought great challenges to the anti-theft work. In order to quickly and accurately locate the suspected users of ”defaulting on electricity consumption and stealing electricity”, based on the massive data of marketing business application system and electricity consumption information collection system, this paper analyzes and studies the existing common means of stealing electricity, and establishes an anti-stealing diagnostic analysis model by combining the correlation analysis algorithm, extracts the abnormal data related to stealing electricity and its prevention and investigation, and accurately locks the suspected users. The aim is to improve the efficiency and accuracy of preventing and investigating electricity theft, so as to effectively curb the occurrence of electricity theft, reduce the line loss rate, ensure the safety of the power grid and improve the efficiency of power supply enterprises. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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