A Multi-source Modeling and Collaborative Analysis Method for Power Stealing Behavior

Abstract

The invention discloses a multi-source modeling and collaborative analysis method for electricity stealing behavior. The multi-source modeling and collaborative analysis method for the electricity stealing behavior includes steps that 1, acquiring data; 2, pre-processing the data; 3, dividing the pre-processed data, and using the divided data as different types of historical consumption behavior of a user; 4, acquiring the current historical data of the user; 5, monitoring the abnormal electricity consumption based on the historical electricity consumption data of the user; 6, collaboratively monitoring the abnormal electricity consumption based on the historical water consumption data of the user; 7, collaboratively monitoring the abnormal electricity consumption based on the historical fuel gas consumption data of the user; 8, collaboratively monitoring the abnormal electricity consumption based on the historical communication consumption data of the user; 9, calculating the comprehensive abnormal electricity consumption monitoring value and judging suspicious users. The multi-source modeling and collaborative analysis method for the electricity stealing behavior is capable of monitoring the electricity stealing behavior of the user at the power supply end, improving the electricity consumption monitoring precision, duly feeding back the possible electricity stealing behavior and lowering the false electricity stealing behavior detecting rate so as to guarantee the normal running of the electric system.

Description

technical field [0001] The invention belongs to the field of electricity stealing analysis, in particular to a method for multi-source modeling and collaborative analysis of electricity stealing behavior. Background technique [0002] With the development of society and the advancement of science and technology, electricity stealing behavior and its technology are changing with each passing day, which brings more difficulties to the supervision of electricity stealing behavior. At this stage, the main way to monitor electricity theft is to use manual door-to-door inspections or install various hardware devices on the user end. However, in the face of the general improvement of the technical capabilities of user groups and the ever-changing technology of electricity theft, the above methods are obviously no longer possible. Meet the monitoring needs of electricity theft. [0003] At present, the constantly innovative power system and open network environment make it possible...

AI Technical Summary

Problems solved by technology

However, the existing electricity theft detection methods can only realize the abnormality detection at the time point of electricity consumption, that is, to give an alarm to a certain instantaneous abnormality of the abnormal electricity consumption of the user.

In real life, abnormal power consumption such as purchase of large-scale electrical appliances and decoration occurs from time to time, and power equipment may also be disturbed and cause short-term fluctuations. Therefore, the above design ideas lead to a large number of misjudgments and greatly increase manual investigation. workload

[0004] In addition, the current electricity theft detection software generally only depends on the user's electricity consumption, and does not establish a consumption model for the user's electricity consumption behavior, which makes these software greatly affected by factors such as seasons and users' personal habits. Therefore, there is an urgent need for more accurate methods to monitor user electricity theft.

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