Meter reading data exception analysis method based on deep learning algorithm

A data anomaly and deep learning technology, applied in neural learning methods, data processing applications, computing, etc., can solve the problems of material and human resources, single method, low accuracy, etc., to achieve deep learning training and intelligent recognition. , The effect of fast and accurate judgment

Pending Publication Date: 2020-04-21
STATE GRID ZHEJIANG ELECTRIC POWER +2
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Problems solved by technology

Historically, on-site detection methods have been used to deal with complaints about abnormal meter reading data, that is, grid technicians go to the power consumption site to conduct investigations. There are also great human

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  • Meter reading data exception analysis method based on deep learning algorithm
  • Meter reading data exception analysis method based on deep learning algorithm
  • Meter reading data exception analysis method based on deep learning algorithm

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[0053] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0054] like figure 1 As shown, a method for abnormal analysis of meter reading data based on deep learning algorithm, the process includes the following steps:

[0055] 1) Feature extraction is performed on the abnormal work order user data set of meter reading data;

[0056] 2) Read the user's meter reading data that has undergone deep learning in the database to determine whether the abnormality is true or not;

[0057] 3) Whether the user’s meter reading data is abnormal is judged by the algorithm model to identify and judge whether it is abnormal, if not, return the judgment result, and if it is, execute the next step;

[0058] 4) According to the characteristics of the daily power consumption data of users with abnormal meter reading data and the business needs of estimating the error power of users with abnormal meter reading...

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Abstract

The invention discloses a meter reading data exception analysis method based on a deep learning algorithm, and relates to the field of power consumer meter reading exception judgment methods. In the past, a field detection method is mostly adopted for processing abnormal opinion complaints of the meter reading data, material resources and human resources are consumed, the efficiency is low, and the problems of single method, low accuracy and the like also exist for judging the determined abnormal user error electric quantity of the meter reading data. According to the method, a BP neural network subjected to deep learning training is adopted to establish a user meter reading data exception subordination judgment algorithm model and an optimal configuration strategy; quick and accurate judgment of work order user meter reading data abnormity affiliation is realized, and accurate estimation of error time and error electric quantity is realized by establishing a meter reading data abnormity user daily electricity consumption prediction model and an optimal configuration strategy. Therefore, the working efficiency of 95598 index analysis and quality control is improved, and an auxiliary decision-making effect is achieved.

Description

technical field [0001] The invention relates to the field of meter reading abnormality determination methods for power users, in particular to a method for analyzing abnormality of meter reading data based on a deep learning algorithm. Background technique [0002] Quickly and accurately handling user complaints about abnormal meter reading data is not only related to the economic interests of the power company, but also related to the service quality of the power company. Historically, on-site detection methods have been used to deal with complaints about abnormal meter reading data, that is, grid technicians go to the power consumption site to conduct investigations. There are also great human factors, which are not conducive to the management of the electric power industry. At the same time, there are also problems such as single method and low accuracy in determining the error power of users with abnormal meter reading data. Contents of the invention [0003] The tec...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q10/067G06Q50/06G06N3/084G06N3/044G06N3/045
Inventor 魏骁雄王正国罗欣陈奕汝沈皓张爽林少娃朱蕊倩朱斌陈博麻吕斌葛岳军钟震远杨建军叶红豆丁嘉涵
Owner STATE GRID ZHEJIANG ELECTRIC POWER
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