Power missing data filling method based on hybrid strategy

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...

AI Technical Summary

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

Images