Electrical load identification method based on improved graph convolutional neural network

Abstract

The invention discloses an electrical load identification method based on an improved graph convolutional neural network, and belongs to the technical field of intelligent power utilization and intrusive load identification, and the method comprises the steps of collecting the power utilization data of a user at a power utilization side, and carrying out the standardized processing of the data; and taking the power consumption data of the user as a training set and a test set of the graph convolutional neural network for advanced training. then, evaluating the overall distribution characteristic, the local trend characteristic and the overall trend characteristic of the load curve of the electrical appliance by applying an Euclidean distance DTW to the acquired load curve, and performing weighted fusion on the three characteristic distribution weights by applying an entropy weight method; and then clustering the load curve of the electrical appliance by adopting a k-means clustering algorithm and applying a method for automatically generating a clustering number K value based on a DBI value as a measurement scale. And finally, taking the clustered electrical appliance load curve asan input set and inputting the input set into a graph convolutional neural network for electrical appliance identification. The trained graph convolutional neural network model identifies the corresponding load curve, and finally draws a probability density distribution curve of the applied electrical appliance.

Description

technical field [0001] The invention belongs to the technical field of intelligent electricity consumption and intrusive load identification, and more specifically relates to an electricity load identification method based on an improved graph convolutional neural network. Background technique [0002] In recent years, with the proposal of smart cities and the continuous development of smart grid technology, the importance of smart power consumption is rising. Smart power consumption is an important part of smart grid, and the power consumption identification technology is the most important aspect of smart power consumption. Accurate monitoring and identification of user electrical appliances can make the power supply side allocate power resources more reasonably, and can improve residents' electricity consumption behavior through load control technology, so as to reduce energy consumption and the maximum load of the power grid, reduce system installed capacity, and reduce p...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problems in the prior art that the quality of the clustering results is too poor, the identification accuracy is low and the intelligence needs to be improved, a method for identifying electricity load based on an improved graph convolutional neural network is proposed.

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