A Hybrid ConvLSTM-Based Anomaly Detection Approach for Combating Energy Theft

In a conventional power grid, energy theft is difficult to detect due to limited communication and data transition. The smart meter along with big data mining technology leads to significant technological innovation in the field of energy theft detection (ETD). This article proposes a convolutional...

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Vydané v:IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 10
Hlavní autori: Gao, Hong-Xin, Kuenzel, Stefanie, Zhang, Xiao-Yu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Anomalies
Artificial neural networks
Big Data
Binary classification
convolutional long short-term memory (ConvLSTM)
Convolutional neural networks
Data mining
Data models
Deep learning
Electric power grids
Electricity
Electricity consumption
Electricity distribution
Electricity meters
energy theft
Feature extraction
Machine learning
Multilayer perceptrons
Performance measurement
Short term
smart grid
Smart grids
Theft
Training
ISSN:0018-9456, 1557-9662
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Shrnutí:In a conventional power grid, energy theft is difficult to detect due to limited communication and data transition. The smart meter along with big data mining technology leads to significant technological innovation in the field of energy theft detection (ETD). This article proposes a convolutional long short-term memory (ConvLSTM)-based ETD model to identify electricity theft users. In this work, electricity consumption data are reshaped quarterly into a 2-D matrix and used as the sequential input to the ConvLSTM. The convolutional neural network (CNN) embedded into the long short-term memory (LSTM) can better learn the features of the data on different quarters, months, weeks, and days. Besides, the proposed model incorporates batch normalization. This technique allows the proposed ETD model to support raw format electricity consumption data input, reducing training time and increasing the efficiency of model deployment. The result of the case study shows that the proposed ConvLSTM model exhibits good robustness. It outperforms the multilayer perceptron (MLP) and CNN-LSTM in terms of performance metrics and model generalization capability. Moreover, the result also demonstrates that <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-fold cross validation can improve the ETD prediction accuracy.
Bibliografia:ObjectType-Article-1
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3201569