Non-intrusive load adaptive identification method based on twin network

A load identification, non-invasive technology, applied in the direction of character and pattern recognition, biological neural network model, neural learning method, etc., can solve the problems of unrecognizable unknown equipment, poor model versatility, poor versatility, etc., to achieve strong model versatility , less training samples, and the effect of improving recognition accuracy

Active Publication Date: 2021-08-13
ZHEJIANG UNIV +1
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Problems solved by technology

[0003] Traditional non-intrusive load recognition algorithms are mostly based on classification models, which mainly have the following disadvantages: First, the models based on supervised learning methods require a large amount of labeled data to train the models, which is often unsatisfactory in reality; and these methods usually cannot To identify unknown equipment, the l

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  • Non-intrusive load adaptive identification method based on twin network
  • Non-intrusive load adaptive identification method based on twin network
  • Non-intrusive load adaptive identification method based on twin network

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Embodiment Construction

[0045] In order to describe the characteristics and effects of the present invention in detail, the present invention will be further described below in conjunction with the accompanying drawings and the PLAID and COOLL data sets.

[0046] (1) First, train the Siamese network model used to obtain acquaintance information, as follows:

[0047] (1.1) Select the house6 data in the PLAID dataset as the training set. House6 includes 6 electrical appliances such as air conditioners, fluorescent lamps, fans (Fans), refrigerators, hair dryers, and laptops. and two working states, in the present invention, it is regarded as a separate device for identification.

[0048] Collect 10 samples from each test case in house6, a total of 360 samples are collected, each including voltage and current data, such as figure 1 Shown in (a) and (b) in ; calculate its active power and plot the V-I trajectory of each sample.

[0049] Preferably, the drawing method of the V-I locus diagram is as follow...

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Abstract

The invention provides a non-intrusive load adaptive identification method based on a twin network, and the method comprises the steps: taking a V-I track and active power of a load as to-be-identified load features, discriminating the similarity of the V-I track of the load through the twin network, and obtaining the load number information through the matching with a feature library, thereby achieving the load identification. Wherein the user establishes the mapping between the load number information and the actual type of the electric appliance according to the stored use time and the actual use condition of the day. Through the dynamic construction of the feature library, the method can achieve the accurate recognition of the unknown load. Finally, the validity and universality of the model are verified in a PLAID data set and a COOLL data set.

Description

technical field [0001] The present invention relates to the field of non-intrusive load monitoring (NILM), in particular to a twin network-based non-intrusive load self-adaptive identification method. Background technique [0002] Understanding user energy consumption is of great significance to load management. In recent years, non-intrusive load monitoring (Non-intrusive load monitoring, NILM) technology has attracted widespread attention. Traditional intrusive load monitoring requires the installation of acquisition and communication devices at each electrical load to detect the load status, and requires modification of existing electrical appliances or lines, which is difficult and expensive to implement. The non-intrusive load monitoring technology monitors the power bus to analyze the status of each load in the line, which has the advantages of strong versatility and low cost. [0003] Traditional non-intrusive load recognition algorithms are mostly based on classific...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06Q50/06G06N3/08G06F119/06
CPCG06F30/27G06Q50/06G06N3/08G06F2119/06G06F18/22G06F18/214
Inventor 于淼王丙楠陆玲霞赵强包哲静程卫东魏萍
Owner ZHEJIANG UNIV
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