A user load classification method based on a basic load reduction strategy

A basic load and load classification technology, applied in data processing applications, instruments, calculations, etc., can solve problems such as wrong identification of user types, and achieve effective clustering and excellent clustering effects

Active Publication Date: 2019-05-10
HOHAI UNIV
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  • Claims
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Problems solved by technology

[0004] The purpose of the present invention is to address the problems existing in the prior art and provide a user load classification method based on the basic load reduction strategy to solve the problem of misidentifying user types due to user scale in the current load clustering method

Method used

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  • A user load classification method based on a basic load reduction strategy
  • A user load classification method based on a basic load reduction strategy
  • A user load classification method based on a basic load reduction strategy

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Embodiment

[0064] The data of the present invention adopts the data of the PJM electric power market in the United States, selects 24-hour load data, and performs calculation example analysis and demonstration on it. In this example, load data from different regions of the PJM electricity market in the United States were selected. In order to eliminate interference from other factors, 113 groups of load data from different regions on a unified working day were selected for cluster analysis.

[0065] (1) The present invention first preprocesses the data to eliminate data with too severe deviations to prevent extreme values ​​from interfering with classification. Define the deviation rate η:

[0066]

[0067] where y i Represents the load data at a certain point, Indicates the average value of load data within 24 hours.

[0068] When η>500%, it is necessary to check whether the data is a bad data point. If most of the load points of the load curve exceed the standard, it is considered...

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Abstract

The invention discloses a user load classification method based on a basic load reduction strategy, and the method comprises the steps: 1, extracting user load data, and carrying out the preprocessingof the load data; Step 2, selecting a load reduction reference value, taking the aggregation degree as an evaluation parameter, and distinguishing load types by adopting a method of reducing a reference load value; and step 3, clustering the load data by adopting a fuzzy C-means algorithm according to a load type distinguishing result. Aiming at the problem that the user type is mistakenly identified due to the user scale in the current load clustering method, the invention provides an improved load clustering algorithm processing skill, and the accuracy of the algorithm provided by the invention is verified by utilizing a fuzzy algorithm. The method adopted by the invention can effectively eliminate the interference of the user scale on the user type.

Description

technical field [0001] The invention belongs to the field of power system load classification, in particular to a user load classification method based on a basic load reduction strategy. Background technique [0002] Load clustering is to integrate a large number of users into different aggregates through certain mathematical means. In view of the real-time operation of the power grid, rationally guiding different types of aggregates to use electricity in an orderly manner can generate huge economic benefits. However, the existing clustering methods generally cluster the direction and value of the load. However, due to the inconsistent scale and number of users in different regions, users of the same type or power consumption pattern cannot be identified, resulting in insufficient classification results. Aiming at the above problems, the present invention proposes a clustering method based on load reduction, and verifies the clustering results of the present invention thr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/06G06K9/62
CPCY04S10/50
Inventor 王敏姜远志石逸张鹏孙鑫源
Owner HOHAI UNIV
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