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A Robust k-means Clustering Method for Power User Segmentation

A technology of power users and clustering methods, applied in the field of robust k-means clustering, which can solve the limitation of labor cost and artificial understanding depth, cannot realize user classification, cannot realize accurate, fast and detailed classification of users, etc. problems, to achieve the effect of reliable clustering results, insensitive selection, and high accuracy

Active Publication Date: 2021-07-20
MEISHAN POWER SUPPLY CO STATE GRID SICHUAN ELECTRIC POWER CO
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the power department classifies user files through subjective judgment, but the limitation of labor cost and artificial understanding depth cannot realize accurate, fast and detailed classification of users, and the purpose is to provide a A robust k-means clustering method for electric power user segmentation, which solves the problem that the electric power sector cannot achieve accurate, fast, and detailed classification of users
The technical problem to be solved by the present invention is that the electric power department classifies user files through subjective judgment, but the limitation of labor cost and artificial understanding depth cannot realize accurate, fast and detailed classification of users, but the present invention proposes The novel and robust k-means clustering method of

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  • A Robust k-means Clustering Method for Power User Segmentation
  • A Robust k-means Clustering Method for Power User Segmentation
  • A Robust k-means Clustering Method for Power User Segmentation

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Embodiment 1

[0025] A kind of robust k-means clustering method oriented to electric power user subdivision of the present invention, comprises the following steps, step 1: extract any group of data sets of electric power company, carry out data standardization processing, described data set is made up of a plurality of clusters; Step 2: Extract the standardized data set, and calculate the dissimilarity between samples in the data set; Step 3: Extract the dissimilarity between samples in the data set in step 2, use the improved MaxMin initialization method to select the initial cluster center, and determine The number and type of cluster centers; step 4: according to the number and type of cluster centers in step 3, automatically split or merge clusters.

[0026] The normalization processing methods in step 1 include maximum and minimum normalization, z-score normalization and decimal scaling normalization.

[0027]In the step 2, the dissimilarity between samples in the data set is calculat...

Embodiment 2

[0052] A robust k-means clustering method for electric power user subdivision, including the following steps, step 1: extract any set of data sets of electric power companies, and perform data standardization processing, the data set is composed of multiple clusters; step 2 : Extract the data set after standardized processing, and calculate the dissimilarity between samples in the data set; Step 3: Extract the dissimilarity between samples in the data set in step 2, use the improved MaxMin initialization method to select the initial cluster center, and determine the cluster The number and type of centers; step 4: according to the number and type of cluster centers in step 3, automatically split or merge clusters.

[0053] The normalization processing methods in step 1 include maximum and minimum normalization, z-score normalization and decimal scaling normalization.

[0054] In the step 2, the dissimilarity between samples in the data set is calculated, and when the data sampl...

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Abstract

The invention discloses a robust k-means clustering method for power user subdivision, comprising the following steps, step 1: extracting any set of data sets of electric power companies, and performing data standardization processing, the data sets are composed of multiple clusters Composition; Step 2: Extract the standardized data set, calculate the dissimilarity between samples in the data set; Step 3: Extract the dissimilarity between samples in the data set in step 2, and use the improved MaxMin initialization method to select the initial clustering center , to determine the number and type of cluster centers; step 4: according to the number and type of cluster centers in step 3, automatically split or merge clusters. The technical problem to be solved by the present invention is that the electric power sector cannot classify users accurately, quickly, and meticulously by subjectively judging user files. However, the novel and robust k-means aggregation proposed by the present invention Class methods can solve this shortcoming.

Description

technical field [0001] The invention relates to a power user subdivision method, in particular to a robust k-means clustering method oriented to power user subdivision. Background technique [0002] Electricity is the most basic source of energy for life and production, and the types of electricity consumers are very complex and diverse. In the traditional mode, the electric power sector classifies user files through subjective judgment. Due to the limitation of labor cost and artificial understanding depth, this method cannot realize accurate, fast and detailed classification of users. Contents of the invention [0003] The technical problem to be solved by the present invention is that the power department classifies user files through subjective judgment, but the limitation of labor costs and artificial understanding depth cannot realize accurate, fast and detailed classification of users. The purpose is to provide a The robust k-means clustering method for power user...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/06G06K9/62
CPCG06Q10/0639G06Q50/06G06F18/23213
Inventor 杨名李强罗海波刘琪琛
Owner MEISHAN POWER SUPPLY CO STATE GRID SICHUAN ELECTRIC POWER CO
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