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Power consumer characteristic analysis method and system based on BEMD and kmeans

A feature analysis, power user technology, applied in data processing applications, character and pattern recognition, other database clustering/classification, etc. Frequency characteristics, poor clustering effect, etc.

Pending Publication Date: 2020-11-06
SHENYANG POLYTECHNIC UNIV +1
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  • Application Information

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Problems solved by technology

[0006] In the process of analyzing the characteristics of power users by traditional clustering algorithms, the present invention can not provide frequency characteristics in local time for nonlinear unsteady signals, resulting in low clustering accuracy and poor clustering effect, and proposes an experience-based Analysis Method of Power User's Characteristics Combining Mode Decomposition and kmeans

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  • Power consumer characteristic analysis method and system based on BEMD and kmeans
  • Power consumer characteristic analysis method and system based on BEMD and kmeans
  • Power consumer characteristic analysis method and system based on BEMD and kmeans

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Embodiment Construction

[0087] Below in conjunction with accompanying drawing, the present invention will be further described:

[0088] The present invention aims at problems such as low clustering accuracy and poor clustering effect due to the inability to provide local time frequency characteristics for nonlinear unsteady signals in the process of analyzing the characteristics of power users by traditional clustering algorithms, and proposes an experience-based An analysis method of power user characteristics combining modal decomposition and kmeans. The invention is applicable to any nonlinear and unsteady power grid load samples, expands data compatibility and improves clustering accuracy at the same time.

[0089] Based on BEMD and kmeans power user characteristic analysis method, the method includes:

[0090] Step 1. Obtain user power load data and store it as a database;

[0091] After a long period of accumulation, each power user in the power collection system has huge and complex load da...

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Abstract

The invention relates to the field of load prediction of power systems, in particular to a power consumer characteristic analysis method and system based on BEMD and kmeans. The method comprises the steps of firstly obtaining user power load data and storing the data as a database; preprocessing the power load data based on an empirical mode decomposition method; carrying out kmeans algorithm clustering on the hierarchical power load data, and selecting a Pearson distance as an evaluation index of a sample similarity degree; According to the kmeans clustering result and the actual user power utilization characteristics, characteristic analysis is carried out for different time intervals. The analysis method can adapt to time interval load data, is also suitable for loads with high volatility and poor stability, can realize hierarchical clustering of power consumer power utilization characteristics, and has a stable and good clustering effect on the premise of considering the operationspeed.

Description

technical field [0001] The invention relates to the field of load forecasting of electric power systems, in particular to a method and system for analyzing power user characteristics based on BEMD and kmeans. Background technique [0002] The analysis of user electricity consumption behavior is based on massive electricity consumption data, taking into account factors such as weather and geographic information, and using data mining technology to extract and analyze the electricity consumption characteristics of different users. The user's electricity load has great uncertainty, and the daily load curve shows the user's electricity consumption behavior in a day, which is manifested as the lateral characteristics of the load; and different daily load curves in a period (such as a week or a month) will also There is a difference, manifested in the longitudinal characteristics of the load. There are obvious differences in the degree of vertical difference among different users...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06F16/906G06K9/62
CPCG06Q10/06393G06Q50/06G06F16/906G06F18/23213Y02D10/00
Inventor 崔嘉商业杨俊友杨超王飞曹智杨壮观王欣柳李桐
Owner SHENYANG POLYTECHNIC UNIV
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