An Anomaly Detection Scheme based on LSTM Autoencoder for Energy Management

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.