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학술대회 An Anomaly Detection Scheme based on LSTM Autoencoder for Energy Management
Cited 11 time in scopus Download 1 time Share share facebook twitter linkedin kakaostory
저자
남홍순, 정연쾌, 박종원
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1445-1447
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289226
협약과제
20PR3800, 스마트시티 에너지 소비 운영 관리를 위한 제어 시스템 개발, 정연쾌
초록
This paper proposes an anomaly detection scheme based on LSTM autoencoder for energy management, which is to prevent anomaly states before they actually occur. When the prognosis of an anomaly state is detected, the anomaly state can be prevented by taking appropriate measures. However, it is difficult to determine normal and anomalous data, since energy consumption varies greatly depending on weather, time, day of the week and season. Thus, this paper proposes an anomaly detection scheme using LSTM autoencoder to detect a data pattern that deviates from the normal data pattern and to determine it as an anomaly state. Experimental results show that this scheme can discriminate anomaly from the observed multivariate data and can be used to prevent fault and incorrect operation in advance.
KSP 제안 키워드
Day of the Week, Detection scheme, Multivariate data, anomaly detection, data patterns, energy consumption, energy management