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A load curve parallel clustering method based on electric power big data

A technology of load curve and clustering method, which is applied in the direction of text database clustering/classification, electrical digital data processing, relational database, etc., can solve the problems such as K-means clustering algorithm calculation is difficult to cope with, and improve the efficiency of load clustering , the effect of increasing speed

Active Publication Date: 2016-09-21
STATE GRID CORP OF CHINA +2
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Problems solved by technology

[0005] However, in the context of big data of electricity consumption information, trillions of daily load curves need to be clustered and analyzed, and the traditional K-means clustering algorithm calculation is difficult to cope with

Method used

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  • A load curve parallel clustering method based on electric power big data
  • A load curve parallel clustering method based on electric power big data
  • A load curve parallel clustering method based on electric power big data

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Embodiment Construction

[0036] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0037] Such as figure 1 As shown, the load curve parallel clustering method based on electric power big data of the present invention comprises the following steps:

[0038] 1. Collect and filter load curves

[0039] The specific method is as follows: extract 96 point load curves of enterprise customers from the database of the energy saving collection system. Enterprise customers refer to enterprise users who have installed load control terminals, delete the curves with incomplete load data and load capacity of 0, and obtain the data A complete and normal load curve for large customers. The 96-point load curve means that the user generally collects 1 point every 15 minutes, and 96 points a day to form a 96-point load curve for a customer.

[0040] 2. Normalize the load curve

[0041] The load power and load capacity of each user in the p...

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Abstract

The invention discloses a load curve parallel clustering method based on big data of electric power. Wavelet denoising is performed on all load curves through a dbN wavelet system in order to reduce effect of small fluctuation in the curves on clustering results, a K means clustering algorithm based on the multi-core parallel technology is adopted to perform load curve clustering, the clustering results with obvious features are screened out, and load curve classification is obtained through confluence analysis. By means of the load curve parallel clustering method based on big data of electric power, the parallel clustering algorithm of a great number of the load curves is achieved, clustering speed of the load curves is increased effectively, and a foundation is laid for load and electricity quantity predication.

Description

technical field [0001] The invention relates to a load curve parallel clustering method based on electric power big data, and belongs to the technical field of electric power marketing intelligent application. Background technique [0002] Information systems such as power marketing, production, and scheduling have generated massive amounts of power information data. Only the Jiangsu power mining system needs to collect the daily load power of more than 30 million residential users in the province and the 96-point power consumption of more than 200,000 large load-control users. The amount of data, a total of more than 30 GB, since 2006 has accumulated electricity consumption information data as much as 39TB. The big data era of Jiangsu Electric Power has come, but how to manage such massive data information, obtain useful information from it, and tap potential value are the challenges and opportunities that Jiangsu Electric Power is facing. [0003] Power system load modeli...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/285G06F16/35
Inventor 郑海雁金农顾国栋丁晓谢林枫熊政徐金玲仲春林方超李昆明季聪
Owner STATE GRID CORP OF CHINA
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