Non-intrusive electrical load decomposition method based on variable weight time domain convolutional network

Abstract

The invention relates to a non-intrusive electrical load decomposition method based on a variable weight time domain convolutional network, and the method comprises the steps: model training: respectively training decomposition models used for the electrical power decomposition of each piece of electrical equipment, the decomposition models comprising a plurality of time domain convolutional networks for the electrical power estimation, and during the training, the time domain convolutional networks are used for the electrical power estimation; for the same electric device, training the corresponding time domain convolutional network by using the electric load data of different time periods; model application: inputting the total electricity utilization power sequence to be decomposed into a decomposition model of each device, and performing electricity utilization power estimation by adopting a plurality of time domain convolutional networks to obtain a plurality of groups of electricity utilization power estimation values of each time point, and carrying out point-by-point variable weight weighted summation on the plurality of groups of electric power estimation values to obtain an electric power decomposition result of the electric equipment at each time point. Compared with the prior art, the method is high in decomposition precision and good in generalization performance.

Description

technical field [0001] The invention relates to the technical field of power metering, in particular to a non-intrusive power load decomposition method based on variable weight time-domain convolutional networks. Background technique [0002] Load decomposition refers to determining the power consumption of specific electrical appliances according to the total power signal used by all the equipment in the room. It can provide detailed electrical information of a single electrical appliance. Compared with intrusive load monitoring that installs sensors on each device, this method has the advantages of low installation cost, less interference to users, and flexible application. It can be widely used in various fields. [0003] Driven by the influence of the current environment, economy, and society, load decomposition is increasingly becoming the focus of researchers. A series of methods based on machine learning and signal processing techniques have been proposed and evaluate...

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However, most of these methods require event detection and feature extraction, and have certain requirement

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