Abnormal power consumption data detection method, system and device, and storage medium

Abstract

The invention provides an abnormal power consumption data detection method. The method comprises the steps of data acquisition, data cleaning, data dimension reduction, model establishment and abnormal user screening. The invention also relates to an abnormal power consumption data detection system, electronic equipment and a storage medium. According to the invention, problems existing in line loss management are effectively solved, data mining research and analysis can be carried out for line loss abnormity of a transformer area power utilization system, line loss management is enabled to bemore transparent and efficient, comprehensive management application of the line loss management can be developed, and finally objectives of energy saving, loss reduction and standardized managementare realized.

Description

technical field [0001] The invention relates to the technical field of electricity consumption information collection, in particular to a method for detecting abnormal electricity consumption data. Background technique [0002] With the rapid development of the information age, the Internet and information and communication industries are the first to carry out research on big data. For the power industry, big data also has far-reaching research significance and bright application prospects. With the gradual evolution of the next-generation power system, the data-driven power supply chain will gradually replace the traditional power supply chain. Among them, the promotion of electricity consumption information collection system provides the necessary data basis for my country's power industry to carry out management and operation decisions and power supply service optimization based on power data analysis. At the same time, with the exponential growth of power consumption ...

AI Technical Summary

Problems solved by technology

Due to the limitation of human, material and financial resources, the deep-seated reasons behind the abnormal data cannot be effectively extracted, but it brings "data disaster" and "data waste"

Therefore, it is difficult to meet the requirements with traditional analysis methods. We need to use data mining to discover the deeper laws of abnormal electricity data, eliminate the contingency of the data, and refine the inevitability of the data.

[0003] Due to the large number of low-voltage customer groups and frequent changes, there are generally abnormal line loss caused by management reasons such as unclear relationship between households and transformers, poor meter reading quality, power theft, and metering failures in line loss management in the station area.

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