Dailyload curve dimensionality reduction clustering method based on kernel principal component analysis

A technology of nuclear principal component analysis and clustering method, which is applied in the direction of instruments, character and pattern recognition, data processing applications, etc., and can solve problems such as weight determination of dimensionality reduction indicators

Active Publication Date: 2019-06-11
HUNAN UNIV
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Problems solved by technology

However, there are still two important problems: 1) the determination of the dimensionality reduction index and its number; 2) the determination of the weight of the dimensionality reduction index

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  • Dailyload curve dimensionality reduction clustering method based on kernel principal component analysis
  • Dailyload curve dimensionality reduction clustering method based on kernel principal component analysis
  • Dailyload curve dimensionality reduction clustering method based on kernel principal component analysis

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Embodiment Construction

[0078] The daily load curve dimensionality reduction clustering method based on nuclear principal component analysis proposed by the present invention is described in detail as follows in conjunction with the accompanying drawings:

[0079] The general train of thought block diagram of the present invention is as figure 1 shown, including the following steps:

[0080] 1) Preprocess the daily load power curve data to obtain the original data matrix A∈R N×m . Among them, N is the number of daily load curves, and m is the dimension, that is, the number of sampling points;

[0081] 2) In combination with the original data matrix A obtained in step 1), a Gaussian radial basis (RBF) kernel function is selected for nonlinear mapping to a high-dimensional feature space to obtain a kernel matrix K and correct it;

[0082] 3) Perform principal component analysis on the revised kernel matrix K to determine the principal component components and the number of dimensionality reduction i...

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Abstract

The invention discloses a daily load curve dimensionality reduction clustering method based on kernel principal component analysis, and the method comprises the steps: carrying out the nonlinear mapping of a preprocessed daily load data matrix to a high-dimensional space through employing a kernel function, and obtaining a high-dimensional kernel matrix; correcting the high-dimensional kernel matrix, performing eigenvalue decomposition to obtain a corresponding eigenvalue and a unitized eigenvector, and setting extraction efficiency according to an eigenvalue descending trend to obtain a principal component and the number of dimension reduction indexes; secondly, taking the eigenvalue as a weight, performing normalization processing with the sum of the eigenvalue and the weight being 1 toobtain a weight vector, and taking the projection of the corrected Gaussian kernel matrix on the principal component as a dimensionality reduction data matrix; and finally, clustering the daily load curve by taking the dimension reduction data matrix and the weight vector as input of a weighting algorithm, and determining an optimal clustering number and a clustering result based on a Silhouette index. According to the method, the clustering quality is improved while the calculation efficiency is improved. And the clustering result is consistent with the reality, so that the method has a certain engineering value.

Description

technical field [0001] The invention belongs to the technical field of power system analysis and control, and mainly relates to a daily load curve dimensionality reduction clustering method based on kernel principal component analysis. Background technique [0002] Daily load curve clustering is the basis of power distribution big data mining, and it has certain guiding significance for load forecasting, power grid planning, and demand-side response. With the continuous advancement of the smart grid, the degree of informatization of the power system continues to increase, and the power consumption information collection system, distribution network GIS system, and distribution network automation system are gradually improved. and other big data features. How to adopt effective data mining technology to finely classify different types of massive users under the background of big data, so as to dig out the internal relationship between different types of loads and the corresp...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q30/02G06Q50/06
CPCY04S10/50
Inventor 李欣然何聪钟伟宋军英汤杰徐飘
Owner HUNAN UNIV
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