The current method of power data consistency test based on gray correlation analysis verifies the consistency of data by measuring the shape and distance between data sequences, which leads to low accuracy of the test due to high data redundancy. In this regard, the consistency check of power fault multi-source heterogeneous big data under common factor structure is proposed. The Kalman filter algorithm is used to reduce the redundancy of power fault data, and the consistency discriminant criterion is established to realize the consistency test of power fault multi-source heterogeneous data by discriminating the cofactor relationship between the data. In the experiments, the proposed method is verified for testing accuracy. The analysis of the experimental results shows that the proposed method is used to test the consistency of power fault data, and its test error is low and has a high test accuracy.
KEYWORDS: Power consumption, Data modeling, Data communications, Data storage, Matrices, Mathematical optimization, Data processing, Data analysis, Statistical modeling, Statistical analysis
In the context of the problem of excessive MAPE values in the method of filling in missing big data of electricity consumption information, a method of filling in missing big data of electricity consumption information based on variational self-encoder is designed. Optimising the electricity information pre-processing model, store and manage the various types of raw and application data collected in a classified manner, define the objective function as the algebraic sum of the squared measurement errors, construct an electricity big data tensor filling model, treat missing values as variables, and design a missing filling method based on a variational self-encoder. Experimental results: The mean value of MAPE of the big data missing fill method for electricity consumption information in the paper is: 38.514%, indicating that the performance of the designed big data missing fill method for electricity consumption information is better after fusing the variational self-encoder.
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