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