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Load power consumption mode identification method

A technology of electricity consumption pattern and identification method, which is applied in the field of electricity consumption analysis of smart grid users, and can solve problems such as poor stability of clustering results, sensitivity of initial clustering centers, and low clustering quality

Inactive Publication Date: 2017-03-22
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, both the classic K-means algorithm and intelligent optimization algorithms such as particle swarm optimization (PSO) have the disadvantage of being sensitive to the initial clustering center and easily falling into local optimum, resulting in low clustering quality. Problems such as poor stability of clustering results

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Embodiment Construction

[0063] The present invention will be further described below in conjunction with specific examples and accompanying drawings, but the protection scope of the present invention is not limited thereto.

[0064] In this application, for the convenience of description, the identification of the electricity consumption pattern of residents' loads is taken as an example for detailed description.

[0065] Such as figure 1 As shown, a pattern recognition method for residential load electricity consumption based on gravity search algorithm includes the following steps:

[0066] S1: Collect residents' electricity load at sampling time interval T, and obtain L daily load curves corresponding to residents in L days;

[0067] S2: Carry out density-based spatial clustering on the obtained daily load curves of residents to obtain the typical load consumption pattern of residents;

[0068] S3: Extract features that describe residents' electricity consumption behavior at different time scale...

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Abstract

The invention relates to a load power consumption mode identification method. The load power consumption mode identification method includes the steps: acquiring the electrical load at a sampling time interval T, and obtaining L daily load curves corresponding to L days of time; performing spatial clustering based on density on the obtained daily load curves, and obtaining a classical load power consumption mode; extracting characteristics describing the power consumption behavior of a user in different time scale; and utilizing a gravitation search algorithm to cluster the obtained power consumption characteristics of the user; repeating clustering, utilizing a cluster evaluation index to evaluate the clustering result, and selecting the optimal clustering result, that is, the identification result of the load power consumption mode. The gravitation search algorithm used by the load power consumption mode identification method has high searching capability and high convergence speed, and is not easy to fall into local optimal solution, and is better than a traditional clustering algorithm on the identification effect, so that identification of the load power consumption mode can be effectively realized and powerful guidance for design of the demand side response scheme, analysis of load characteristics and high-accuracy prediction can be provided.

Description

technical field [0001] The invention relates to a load power consumption pattern recognition method, which belongs to the technical field of power consumption analysis of smart grid users. Background technique [0002] With the continuous development of the smart grid, the penetration rate of smart meters is getting higher and higher. As an important part of the advanced measurement system of the smart grid, the smart meter is a key device connecting residents and the grid, and an important interface for understanding residents' electricity consumption. How to use a large number of residents' electricity consumption data collected by smart meters to mine them to obtain effective information that can help improve operational reliability and economic and social benefits is an urgent problem that needs to be solved in the process of transforming traditional power companies into comprehensive energy service providers. one of the important issues. [0003] The electricity consu...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 王飞李康平汪新康刘力铭
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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