Residential electricity consumption data missing value interpolation method based on neighbor algorithm

Abstract

In the residential electricity consumption safety management, accurate residential electricity consumption data has very important purposes in data mining, and is a primary basis for realizing residential electricity consumption analysis and residential electricity consumption safety management by establishing a data mining model through linear regression, grey prediction and other algorithms. Theinvention discloses a method for supplementing missing data to residential electricity consumption data on the basis of a K-nearest neighbor (KNN) algorithm. The missing data caused by various reasons in the residential electricity consumption data can be effectively filled, the purpose of improving the data quality in the data mining application model is achieved, and a better decision-making data basis is provided for urban managers.

Description

technical field [0001] The present invention relates to the technical field of power load forecasting, in particular to a KNN (k-nearst neighbors, K-nearest neighbors algorithm)-based method for supplementing missing data for residential electricity consumption data. Background technique [0002] In recent years, group renting in the community and the industrial application of residential electricity have emerged one after another. The management of residential electricity is a new challenge for city managers. In big data and intelligent management, through the in-depth analysis of residents' electricity consumption data, residents' electricity consumption portraits, habit analysis and residential electricity safety analysis can help city managers find such violations in the first time when abnormalities occur. Condition. Residential electricity safety analysis aims to collect a large number of residential electricity load data through terminal equipment such as smart meter...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to various situations in practice, there are many difficulties in the collection of residential electricity consumption data, which will cause incompleteness of electricity consumption data.

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