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Power missing data filling method based on hybrid strategy

A technology with missing data and mixed strategies, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as increasing the amount of algorithm calculation, filling accuracy to be improved, modeling without data itself, etc., to improve accuracy , Simplify the difficulty of calculation and improve the effect of filling accuracy

Active Publication Date: 2018-11-13
GUANGDONG POWER GRID CO LTD +1
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Problems solved by technology

[0009] Defects of the above-mentioned patent scheme 1: This method designs a solution based on the traditional k-Means algorithm for the problem of missing data filling, which solves the problem to a certain extent, but does not overcome some defects of the k-Means algorithm itself, and uses aggregate There is no way to learn the inherent laws of the data for data filling, and the filling accuracy needs to be improved
[0010] The defect of the above-mentioned patent scheme 2: This method designs an incomplete data filling scheme based on k-Means clustering and deep autoencoder, which solves the problem to a certain extent, but only considers clustering when filling data The resulting full data weighted average does not model the data itself
Moreover, the use of deep autoencoders and backpropagation calculations will increase the computational complexity of the algorithm

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  • Power missing data filling method based on hybrid strategy
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  • Power missing data filling method based on hybrid strategy

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Embodiment Construction

[0044] The accompanying drawings are for illustrative purposes only, and should not be construed as limitations on this patent; in order to better illustrate this embodiment, certain components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The positional relationship described in the drawings is for illustrative purposes only, and should not be construed as a limitation on this patent.

[0045] figure 1 It is an overall flow chart of the present invention, comprising the following steps:

[0046] S1. Using the improved k-Means clustering algorithm to cluster the data sets containing missing data;

[0047] S2. Improve and construct the RBF neural network according to the clustering results;

[0048] S3. Train the RBF neural network, and perform a filling test on the...

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Abstract

The invention relates to the technical field of power data cleaning, in particular to a power missing data filling method based on a hybrid strategy. The method comprises the steps that S1, an improved k-Means clustering algorithm is adopted to perform clustering on a dataset containing missing data; S2, an RBF neural network is improved and constructed according to the clustering result; and S3,the RBF neural network is trained, and filling checking is performed on the missing data. Through the method, clustering of the dataset with missing properties is well realized, and the RBF neural network is designed in combination with the clustering result to perform prediction filling on missing values. Besides, the filling precision of the missing data is improved, implementation is easy and convenient, calculation expenditure is appropriate, and the method has high practical value against the problem that a large amount of data generated in the operation and maintenance process of a powersystem is missed and damaged due to the influences of physical factors, software factors and other factors.

Description

technical field [0001] The present invention relates to the technical field of electric power data cleaning, and more particularly, relates to a method for filling missing electric power data based on a hybrid strategy. Background technique [0002] With the development of computer science, more and more traditional industries are combined with computer applications. Under the development trend of big data and artificial intelligence, the research on the power industry has produced more new ideas. A large amount of data will be generated in the process of operation and maintenance of the power system, and the problem of data loss will occur due to the influence of physical factors and software factors in the process of data collection, data storage, analysis and classification. Data loss is a complex problem in many research fields. For data mining, the existence of missing values ​​has the following effects: the system loses a lot of useful information; the uncertainty sh...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/23213
Inventor 曾瑛李星南李伟坚林斌刘新展张正峰
Owner GUANGDONG POWER GRID CO LTD
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