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Anomaly detection is concerned with a wide range of applications such asfault detection, system monitoring, and event detection. Identifying anomaliesfrom metering data obtained from smart metering system is a critical task toenhance reliability, stability, and efficiency of the power system. This paperpresents an anomaly detection process to find outliers observed in the smartmetering system. In the proposed approach, bi-directional long short-termmemory (BiLSTM) based autoencoder is used and finds the anomalous data point.It calculates the reconstruction error through autoencoder with thenon-anomalous data, and the outliers to be classified as anomalies areseparated from the non-anomalous data by predefined threshold. Anomalydetection method based on the BiLSTM autoencoder is tested with the meteringdata corresponding to 4 types of energy sources electricity/water/heating/hotwater collected from 985 households.

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