Electricity stealing detection method and system based on ResNet-LSTM

Abstract

The invention discloses an electricity stealing detection method and system based on ResNetLSTM; and the method comprises the steps: collecting an electricity consumption data sample of a user and anelectricity consumption type label of the sample, which are collected during the normal operation of a power system, carrying out data preprocessing of electricity consumption data, and enabling a data set to be divided into a training set, a testing set and a verification set; using an automatic encoder to process the labels in the training set as electricity utilization data samples for electricity stealing, and obtaining a new training set; respectively inputting the power consumption data of the original training set into a ResNet model and an LSTM model to carry out an electricity larcenydetection test, selecting a ResNet and LSTM neural network combined structure through a test result, building a hybrid neural network according to the ResNet and LSTM neural network combined structure, and selecting a proper hybrid neural network structure through the test; performing testing by using the selected ResNet-LSTM hybrid neural network structure to select an appropriate neural networkoptimization method, and forming an electricity stealing detection model. The new training set is applied to train the electricity stealing detection model, a complete electricity stealing detectionmethod is constructed, and the electricity stealing detection capability and the detection efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of data mining of power systems, and relates to a ResNet-LSTM-based electricity stealing detection method and system. Background technique [0002] With the gradual establishment of the Advanced Metering Infrastructure (AMI) system, while bringing massive data to relevant departments, it also puts forward higher requirements for the operation and management of the power system. According to a research report released by the US official agency after the basic establishment of its AMI system, according to the relevant statistics of a US power company, the economic benefits lost by the company due to users tampering with the data in the smart meter metering device can reach 400 million Dollar. The same problem was encountered in the process of promoting the establishment of the AMI system in China. In the first cracked smart meter electricity theft case in China, the user used high-tech means to make electrici...

AI Technical Summary

Problems solved by technology

In the face of high-tech electricity stealing methods for smart meter communication and storage functions, it is difficult for hardware detection methods to have a better mining potential

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