Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder

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Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical task to enhance reliability, stability, and efficiency of the power system. This paper presents an anomaly detection process to find outliers observed in the smart metering system. In the proposed approach, bi-directional long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous data point. It calculates the reconstruction error through autoencoder with the non-anomalous data, and the outliers to be classified as anomalies are separated from the non-anomalous data by predefined threshold. Anomaly detection method based on the BiLSTM autoencoder is tested with the metering data corresponding to 4 types of energy sources electricity/water/heating/hot water collected from 985 households. © 2022 IEEE.
Publisher
IEEE Consumer Technology Society
Issue Date
2022-01-07
Language
English
Citation

IEEE 40th International Conference on Consumer Electronics

ISSN
0747-668X
DOI
10.1109/ICCE53296.2022.9730398
URI
http://hdl.handle.net/10203/291955
Appears in Collection
GT-Conference Papers(학술회의논문)
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