Group rental house identification method based on fine-grained energy consumption data

A recognition method and fine-grained technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of misidentification of large water and electricity users, high cost, difficult operation, etc., achieve comprehensive weight, improve living and use. Electrical safety, strong visualization effect

Active Publication Date: 2021-12-21
STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT +2
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Problems solved by technology

The former is difficult to operate and requires high cost; the latter uses ...

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  • Group rental house identification method based on fine-grained energy consumption data
  • Group rental house identification method based on fine-grained energy consumption data
  • Group rental house identification method based on fine-grained energy consumption data

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[0105] The application will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.

[0106] Such as figure 1 As shown, a group rental house identification method based on fine-grained energy consumption data of the present invention includes the following steps:

[0107] Step 1: Integrating the electrical functions, usage quantity, and usage frequency of residential users, conduct hierarchical subdivision of electrical appliances, and select subdivided electrical appliances for group rental housing identification;

[0108] During the specific implementation, the electrical appliances are subdivided into two levels based on the functions, quantity, and frequency of use of electrical appliances, as shown in Table 1.

[0109] Table 1

[0110]

[0111] The total electricity consu...

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Abstract

The invention discloses a group rental house identification method based on fine-grained energy consumption data, and the method comprises the steps: 1, carrying out the hierarchical subdivision of electric appliances through integrating the electric appliance functions, the use number and the use frequency of residential users, and selecting subdivided electric appliances for group rental house identification; 2, acquiring fine-grained energy consumption data of the subdivided electric appliance selected in the step 1 and performing preprocessing; 3, extracting electricity consumption behavior characteristics of the residential users, wherein the electricity consumption behavior characteristics comprise electricity consumption characteristics, variation characteristics and fluctuation characteristics; 4, configuring the characteristic index weight of the power consumption behavior characteristics extracted in the step 3 by adopting a CRITIC weight method; and 5, based on the power consumption behavior characteristic values and the weights thereof, analyzing the power consumption behavior characteristic quantity of each cluster in a clustering result by adopting a clustering algorithm based on an SOM neural network, and identifying suspected group house renting users and normal resident users. According to the invention, assistance can be provided for screening and fighting the group renting phenomenon, and the electricity utilization and living safety of residents can be improved with assistance.

Description

technical field [0001] The invention belongs to the technical field of electric power big data application, and relates to a group rental house identification method based on fine-grained energy consumption data. Background technique [0002] Group rental housing is a "stubborn disease" in the process of urban development, and public safety accidents and community disputes caused by it are not uncommon. Governments in many places have introduced relevant policies and measures to rectify group rental housing, but the phenomenon of group rental is still repeated. [0003] There are two types of existing group rental houses: one is to statistically record the flow of people entering and leaving the community; the other is to conduct statistical analysis on the consumption of water and electricity. The former is difficult to operate and requires high costs; the latter uses too rough data, which is easy to cause misidentification of large hydropower users. Contents of the inve...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/06G06N3/08
CPCG06N3/061G06N3/08G06N3/045G06F18/213G06F18/23Y02D10/00
Inventor 黄艺璇杨世海栾开宁周赣黄奇峰陈铭明刘恬畅方凯杰程含渺曹晓冬李志新孔月萍吴亦贝苏慧玲陆婋泉陈宇沁李波储娜娜张娅楠
Owner STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT
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