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Conference Paper
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder
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- Authors
- Sangkeum Lee, Hojun Jin, Sarvar Hussain Nengroo, Yoonmee Doh, Chungho Lee, Taewook Heo, Dongsoo Har
- Issue Date
- 2022-01
- Citation
- International Conference on Consumer Electronics (ICCE) 2022, pp.1-6
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Bi-directional, Critical task, Detection Method, Energy sources, Event detection, Long-short term memory(LSTM), Metering data, Power system, Reconstruction Error(RE), Smart Metering System, System monitoring