Electricity consumption information acquisition data exception analysis method based on isolated forest algorithm

Abstract

The invention discloses an electricity consumption information acquisition data exception analysis method based on an isolated forest algorithm, and the method comprises the steps of building a transformer area line loss management index based on an electricity consumption information acquisition system, and formulating a transformer area line loss management method based on the electricity consumption information acquisition system; for the line loss type transformer areas, adopting a cloud storage technology to realize acquisition, classification and processing of the power utilization information data of the line loss type transformer areas; analyzing and summarizing the type of the dirty data, eliminating the noise according to the representation form of the dirty data, and removing the dirty data; converting the cleaned and screened data into a form beneficial to data mining through data conversion; establishing a data analysis model by applying the isolated forest algorithm; andperforming model evaluation by applying the ROC curve and the area AUC under the curve and the cumulative recall ratio curve and the P-R curve of the subject, applying the model to a plurality of lineloss type transformer area power consumption information data sets, performing data mining on the screened data, and screening the power consumption abnormal users. According to the method, the dataabnormal users are effectively mined by adopting the isolated forest algorithm, the line loss reasons are analyzed, and the line loss management of the transformer areas is enhanced.

Description

technical field [0001] The invention relates to the technical field of electricity consumption information collection, in particular to an abnormal analysis method for electricity consumption information collection data based on an isolated forest algorithm. Background technique [0002] With the rapid development of the information age, the Internet and the information and communication industry 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 based on power data analysis and power supply service optimization. At t...

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, and it is necessary 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 household changes, poor meter reading quality, power theft, and metering failures in line loss management in the station area.

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