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학술지 Anomaly detection of smart metering system for power management with battery storage system/electric vehicle
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저자
이상금, Sarvar Hussain Nengroo, 진호준, 도윤미, 이충호, 허태욱, 하동수
발행일
202308
출처
ETRI Journal, v.45 no.4, pp.650-665
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2022-0135
협약과제
21PR6100, 가정 에너지 사용량 실시간 진단 및 지능형 자율제어/관리 시스템 원천기술 개발, 도윤미
초록
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.
KSP 제안 키워드
Battery storage system, Bidirectional Long Short-Term Memory, Convolutional networks, Electricity cost, Learning model, Long short-term memory network, Long-short term memory(LSTM), Missing values, Multi-objective optimization(MOP), Real-Time, Smart Metering System
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