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

Autor:	Lee, Sangkeum, Jin, Hojun, Nengroo, Sarvar Hussain, Doh, Yoonmee, Lee, Chungho, Heo, Taewook, Har, Dongsoo
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning FOS: Electrical engineering electronic engineering information engineering Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG)
Zdroj:	2022 IEEE International Conference on Consumer Electronics (ICCE).
Popis:	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. 6 pages, 6 figures, accepted by "IEEE 40th International Conference on Consumer Electronics"
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37cbba627ec70fdee9c3bb9d6d37c553 https://doi.org/10.1109/icce53296.2022.9730398 Zobrazit plný text záznamu