논문 해외 국제전문학술지(SCI급) Anomaly Detection of Smart Metering System Employing Bi-Directional LSTM Network Autoencoder for Power Management with BSS/EV
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2023-08
- 저자명 Lee, Sangkeum; Nengroo, Sarvar; Jin, Hojun; Doh, Yoonmee; Lee, Chungho; Heo, Taewook; Har, Dongsoo
- 학술지명 ETRI Journal
- 발행국가 해외
- 전체저자수 7
- 원문 링크 https://doi.org/10.4218/etrij.2022-0135
홈페이지논문 초록 (Abstract)
A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.