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Random forest-based non-invasive home appliance identification method

A technology for household appliances and random forests, which is applied in character and pattern recognition, instruments, and measurement of electrical variables, etc., can solve the problems that installation work needs to enter the interior of the appliance, affect production work, and is not suitable for promotion, so as to overcome overfitting and Data set imbalance problem, good robustness, and the effect of fast recognition

Inactive Publication Date: 2017-10-20
XI AN JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

The advantage of this method is that the measurement is accurate, but the disadvantage is that the investment is large, and the installation work needs to enter the interior of the electrical appliance, which affects the normal production work, and is not suitable for comprehensive promotion.

Method used

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  • Random forest-based non-invasive home appliance identification method
  • Random forest-based non-invasive home appliance identification method
  • Random forest-based non-invasive home appliance identification method

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Experimental program
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Embodiment Construction

[0061] The present invention will be further explained below in conjunction with specific embodiments and accompanying drawings.

[0062] see figure 1 , the present invention comprises the following steps:

[0063] 1) Establish a load characteristic database, which stores a variety of load characteristics of various household appliances: each appliance in the characteristic database stores a set of multidimensional data, mainly including the steady-state characteristics, transient characteristics and operating mode characteristics of the electrical appliances. kind;

[0064] 2) Use the load feature stored in the feature database as the original training set, and generate N training subsets from the original training set through Bagging sampling technology;

[0065] 3) Perform recursive analysis on each training subset to generate a decision tree: the generated decision tree is an inverted tree structure. The decision tree takes the training subset as the root node, adopts a...

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Abstract

The invention discloses a random forest-based non-invasive home appliance identification method, which aims to monitor a family internal power utilization condition without invading an interior of a family user, and is high in identification speed and high in accuracy. According to the adopted technical scheme, the method comprises the steps of establishing a load feature database; taking load features stored in the feature database as an original training set, and generating N training sub-sets in the original training set; combining N generated decision trees into a random forest; finishing a training process of the random forest through optimization of weights of different decision tree leaf nodes; detecting an electric appliance switching event by utilizing a secondary detection algorithm to obtain starting and ending time of event occurrence, and separating out current and voltage signals of a switched electric appliance from a bus signal, thereby obtaining the load features from separated data; and finally inputting the load features as input parameters to the trained random forest, and finishing electric appliance identification through voting.

Description

technical field [0001] The invention belongs to the field of electric power monitoring, and relates to an online monitoring method for household appliances, in particular to a random forest-based non-invasive household appliance identification method. Background technique [0002] Power load monitoring refers to the technology of collecting electrical parameters generated during load operation through smart meters, and then analyzing its operating status. For home users, load monitoring can enable users to know the time-sharing power, start and stop time of each type of electrical appliances, and guide electricity consumption behavior based on this information, so as to achieve the purpose of energy saving. For power companies, through the monitoring of household electricity consumption, a typical user data platform is established to carry out fine modeling, analysis, classification, and formulation of power consumption behaviors of different users, so as to realize the rati...

Claims

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Application Information

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IPC IPC(8): G06K9/62G01R22/00G01R11/50
CPCG01R11/50G01R22/00G06F18/24323
Inventor 陈琨田旭光刘虎杨志明冯增行
Owner XI AN JIAOTONG UNIV
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