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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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 10
Main Authors:	Gao, Hong-Xin, Kuenzel, Stefanie, Zhang, Xiao-Yu
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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