Distributed clustering method for mass load curves

A distributed clustering and load curve technology, applied in structured data retrieval, instruments, electronic digital data processing, etc., can solve the problems of large computing resources, low efficiency, long processing time, etc. demand, the effect of reducing storage costs

Inactive Publication Date: 2016-03-30
ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a distributed clustering method for massive load curves to solve the problems of low efficiency, long processing time, large consumption of computing resources, data communication and storage costs in existing load curve clustering methods

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  • Distributed clustering method for mass load curves
  • Distributed clustering method for mass load curves
  • Distributed clustering method for mass load curves

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Embodiment Construction

[0033] like Figure 1 to Figure 3 As shown, the distributed clustering method of massive load curves of the present invention includes:

[0034] Step S1, divide all users in the clustered area into M user subsets, and set up a local data center corresponding to each user subset, and use each local data center to collect the preset days of each user in the corresponding user subset The original daily load curve in , where M is a positive integer greater than 1, each user subset contains at least one user, and the number of load collection points included in each original daily load curve is T, for example: the user is monitored every hour In the case of one load collection, T=24;

[0035] Wherein, the user subset division of all users in the clustered area may be performed according to the characteristics of the users in the clustered area, for example, it may be divided according to the region where the users are located.

[0036] Step S2, using each local data center to per...

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Abstract

The present invention discloses a distributed clustering method for mass load curves. According to the method, all users in a clustered area are partitioned into M user subsets, a local data center is correspondingly set for each user subset, each local data center is used for respectively performing adaptive local clustering on a respective daily load curve obtained by processing so as to reduce to-be-analyzed electrical data, then a global data center is set corresponding to the clustered area, and the global data center performs global clustering analysis on all received local typical curves, thereby enabling each original daily load curve of each local data center to be attributed to a corresponding global cluster. According to the method disclosed by the present invention, under the condition of ensuring preset clustering accuracy, clustering efficiency of mass daily load curve electrical data that is high in volume and wide in distribution can be improved effectively, data processing time can be reduced, the requirement on memory calculation can be reduced and communication overheads and storage cost of data can be lowered.

Description

technical field [0001] The invention relates to a distributed clustering method for massive load curves, which belongs to the field of big data processing of electricity consumption in the electric power industry. Background technique [0002] With the popularization of smart meters, the power system is more and more capable of collecting electricity consumption information of users. Different from the traditional monthly meter reading, smart meters can collect and store users' electricity consumption data at a higher frequency. Hourly or even finer-grained electricity consumption data provide a rich source of information for analyzing consumer electricity consumption behavior. Mining user electricity consumption data and effectively identifying user electricity consumption patterns are of great significance in evaluating demand response potential, improving load forecasting accuracy, and guiding electricity price formulation. [0003] Obtaining typical load curves through ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q50/06
CPCG06F16/285G06Q50/06Y02D10/00
Inventor 林国营杨骏伟谭跃凯曾智健朱文俊罗敏阙华坤谭伟聪王毅
Owner ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
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