Non-intrusive household appliance load identification method based on adaptive feature selection

A non-intrusive, load identification technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as prone to misjudgment, achieve the effect of reducing event misjudgment, reducing misjudgment, and simple principle

Pending Publication Date: 2021-04-30
XIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a non-intrusive home appliance load identification method based on adaptive feature selection, which solves the problem of easy misjudgment in the prior art and improves the accuracy of identification

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  • Non-intrusive household appliance load identification method based on adaptive feature selection
  • Non-intrusive household appliance load identification method based on adaptive feature selection
  • Non-intrusive household appliance load identification method based on adaptive feature selection

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Embodiment Construction

[0078] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0079] The present invention is a non-intrusive household appliance load identification method based on adaptive feature selection, such as figure 1 As shown, the specific steps are as follows:

[0080] Step 1. Data preprocessing, denoising the selected REDD data set;

[0081] Step 1 is specifically implemented according to the following steps:

[0082] Step 1.1, selection of data set, select the REDD public data set for testing, which contains data of six families for about three weeks, represented as high-frequency data of 15kHz and low-frequency data of 1Hz second level;

[0083] Step 1.2, denoising processing of the power signal. Since isolated noise points are easily misidentified as events by the event detection algorithm, the median filtering method is selected to process the original power signal to ensure that it does not change th...

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Abstract

The invention discloses a non-intrusive household appliance load identification method based on adaptive feature selection, which is specifically implemented according to the following steps: data preprocessing: denoising a selected REDD data set; performing event detection on the processed data through an improved generalized likelihood ratio test; extracting multi-dimensional load characteristics of the detected event change points; carrying out segmentation according to the power, and for multi-dimensional characteristics, using an adaptive mRmR algorithm to extract characteristics corresponding to each segment; taking the selected characteristics as load marks, and establishing a load feature library through an improved K-means algorithm; and on the basis of the load characteristic library, using the kNN algorithm to identify the load working state of the household electrical appliances in the user. The problem that in the prior art, misjudgment is likely to happen is solved, and the recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of home appliance load identification, and relates to a non-invasive home appliance load identification method based on adaptive feature selection. Background technique [0002] With the continuous advancement of a strong smart grid and a new round of power system reform, the use of non-intrusive load monitoring (NILM) technology to fully mine power consumption information has theoretical guiding significance for user behavior analysis and two-way real-time interaction between users and the grid. [0003] Non-intrusive home appliance load identification is a user-oriented non-intrusive load monitoring technology. Its process can be summarized into four steps: data acquisition, event detection, feature extraction, and load identification. In event detection, the probability model represented by the generalized likelihood ratio test (GLR) is commonly used for its rigorous mathematical derivation and the princi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/245G06F16/215G06F16/28
CPCG06F16/245G06F16/215G06F16/285
Inventor 张志禹周咪
Owner XIAN UNIV OF TECH
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