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Method for supplementing missing values by applying Bayesian estimation in residential electricity consumption data mining

A technology of Bayesian estimation and residential electricity consumption, applied in data processing applications, special data processing applications, electrical digital data processing, etc., can solve problems such as uncontrollable, affecting accuracy, and loss

Pending Publication Date: 2020-11-20
上海积成能源科技有限公司
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Problems solved by technology

Some algorithms in data mining assume that all values ​​are numerical and contain meaning. When these missing values ​​are introduced into the data mining model, it will bring uncontrollable influence and loss of accuracy to the analysis results of the model.

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  • Method for supplementing missing values by applying Bayesian estimation in residential electricity consumption data mining

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[0006] In order to make the content, purpose, characteristics and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the implementation described below Examples are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of the protection specification of the present invention, such as figure 1 The implementation steps of the present invention are shown as follows.

[0007] step one, Data preprocessing: Arrange the collected original residential electricity consumption data in time series, determine the start and end time of the data set, check the default of the data in the time series, mark ...

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Abstract

In residential electricity consumption data management and analysis, the requirement for data integrity is very high, missing values in collected original data need to be supplemented through variousmathematical methods, and the effectiveness of the data is kept. The invention discloses a method for supplementing missing values by applying Bayesian estimation in residential electricity consumption data, which can effectively supplement missing data in residential electricity consumption data through a series of mathematical calculations so as to achieve the purposes of improving data qualityand ensuring data integrity.

Description

technical field [0001] The invention relates to the technical field of electric load forecasting, in particular to a method for supplementing missing data by applying Bayesian estimation to missing values ​​in residential electricity consumption data in residential electricity consumption data mining. Background technique [0002] Residential electricity consumption is affected by many factors, and mastering the laws of residents' electricity consumption habits and their main influencing factors is of great significance for power system scheduling, the promotion of electricity marketization, and intelligent city management. The first step to analyze and mine residential electricity consumption data is to collect complete and effective residential electricity consumption data. However, the data set of residential electricity consumption data will contain missing values ​​due to various reasons (such as data loss caused by emergencies, etc.), and these missing values ​​are usu...

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Application Information

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IPC IPC(8): G06F16/215G06Q30/02G06Q50/06
CPCG06F16/215G06Q30/0201G06Q50/06Y02D10/00
Inventor 周浩顾一峰胡炳谦韩俊
Owner 上海积成能源科技有限公司
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