Electric quantity missing value filling method based on multi-parameter Internet of Things fusion technology

Abstract

The invention provides an electric quantity missing value filling method based on a multi-parameter internet-of-things fusion technology, and relates to the technical field of electric power metering. The method comprises the following steps: building a multi-parameter internet-of-things fusion frame, and collecting known attribute data related to the electric quantity; acquiring electric quantity data; performing noise reduction on the data, then calculating a correlation coefficient value between each piece of known attribute data and the electric quantity data, screening out the known attribute data of which the correlation coefficient value is greater than a threshold value and performing subsequent steps, and enabling the rest of the known attribute data not to participate in the subsequent steps; performing clustering analysis on the known attribute data to obtain a similar scene set of the known attribute data; calculating the weight of each piece of known attribute data; calculating the similarity, and calculating the comprehensive similarity by combining the weights; and searching a time period with the highest comprehensive similarity, and selecting the electric quantity data of the time period to carry out missing value filling. The accuracy of missing value filling is improved by fully utilizing the electric quantity related attribute data.

Description

technical field [0001] The invention belongs to the technical field of electric power metering, and in particular relates to a method for filling missing electric power values ​​based on multi-parameter IoT fusion technology. Background technique [0002] With the widespread application of power information collection systems and advanced metering infrastructure (AMI), the intelligent collection of power data has achieved full coverage. However, in practical applications, the lack of frequency and precision in the collection process of electricity data inevitably leads to some missing values ​​in the data, which interferes with the data analysis process and affects the final recognition effect of the model. Therefore, how to effectively fill in the missing values ​​of user data on the distribution network side has become a major problem. [0003] At present, the common method of filling the missing value of electricity is to use the default value, mean value or mode to fill...

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Problems solved by technology

[0003] At present, the common method of filling the missing value of electricity is to use the default value, mean value or mode to fill the missing value, but the missing value filled by this method is not accurate enough, the filling error is large, the noise of the data is artificially increased, and the filling effect is not ideal. , there is an urgent need for a reasonable and effective method to accurately fill in the missing value of electricity to meet the needs of data analysis and data application

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