Non-intrusive load adaptive identification method based on twin network

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...

AI Technical Summary

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 load identification model based on the classification method can only identify the learned loads, but cannot identify new loads and unknown loads; third, the model has poor versatility, and the types of loads in different families are not the same. Traditional load identification The method can only be modeled and optimized for specific situations, and its versatility is poor

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