Non-intrusive load identification method based on metric learning

A non-invasive, load identification technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as poor model generalization performance, increased maintenance complexity and cost, and inability to identify new loads, etc., to achieve strong Generalization performance, reduced migration cost, and high practical value

Pending Publication Date: 2022-04-15
ZHEJIANG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

First of all, most of these models rely on a large number of labeled samples for model training, but in practical applications, it is often impossible to obtain enough labeled data or the acquisition cost is high; secondly, these models usually assume that all loads in the scene are known, and new loads are added to the scene Afterwards, the original model cannot recognize the new load, and even affects the recognition effect of the original load; finally, these models have poor generalization performance, and often need to retrain the model after changing the scene. Training and building a model for each scene separately will Lead to greatly increased maintenance complexity and cost

Method used

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  • Non-intrusive load identification method based on metric learning
  • Non-intrusive load identification method based on metric learning
  • Non-intrusive load identification method based on metric learning

Examples

Experimental program
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Effect test

Embodiment 1

[0083] In order to illustrate the general recognition ability of the present invention, use 80% WHITED data set samples as training set to train feature extraction network, test in the remaining samples, and common V-I method: Literature [1] (De Baets L, Dhaene T, Deschrijver D ,et al.VI-Based Appliance Classification Using Aggregated PowerConsumption Data[C] / / 2018 IEEE International Conference on Smart Computing(SMARTCOMP).Taormina:IEEE,2018:179–186.), Literature [2](Wang Ying, Yang Wei, Xiao Xianyong, Zhang Shu. Non-intrusive Residential Load Monitoring Method Based on Refined Identification of U-I Trajectory Curve [J]. Power Grid Technology, 2021, 45(10): 4104-4113.) and literature [3] (De Baets L ,Ruyssinck J,Develder C,et al.ApplianceClassification Using VI Trajectories and Convolutional Neural Networks[J].Energy and Buildings,2018,158:32–36.) For comparison, use F 1 The scores are used as performance indicators, and the results are shown in Table 1.

[0084] Table 1 Com...

Embodiment 2

[0088] In order to verify the generality and small-sample learning ability of the method of the present invention, only the samples of house6 in the PLAID dataset are used as the training set, and some COOLL datasets are selected for testing.

[0089] The electrical appliances selected in the COOLL data set include air conditioners, modems, chargers, travel chargers, drilling machines, fans 1, fan 2, soldering irons, and vacuum cleaners. Collect 20 sets of information for each electrical appliance as a test sample and get the recognition result. The confusion matrix is ​​attached Figure 6 shown.

[0090] Due to signal noise or current fluctuations during steady-state operation of electrical appliances, some electrical appliances are identified as multiple numbers, as shown in the confusion matrix, electrical appliance 1 and electrical appliance 5 respectively recognize two numbers, that is, the waveform difference within the class exceeds The threshold set by the model, cert...

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Abstract

The invention provides a non-intrusive load identification method based on metric learning, and the method comprises the steps: mapping load current characteristics to a metric space through a one-dimensional convolutional neural network, achieving the clustering of the characteristics through the triple loss during network training, and carrying out the similarity discrimination of the characteristics of the metric space, thereby achieving the load identification. According to the method, the unknown load can be effectively identified, and the generalization ability is high; on the other hand, metric learning serves as one of small sample learning methods, dependence on training samples can be relieved, and high practicability is achieved.

Description

technical field [0001] The invention relates to the field of non-intrusive load monitoring (NILM), in particular to a non-intrusive load identification method based on metric learning. Background technique [0002] With the rapid development of society, the demand for energy continues to increase, and electric energy, as the main secondary energy, is one of the main energy utilization methods. How to improve the efficiency of electric energy use and realize intelligent power consumption has attracted widespread attention, and it is particularly important to grasp the detailed power consumption information on the user side. 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 analyzes the status of each load in the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/774G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214
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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