Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder
This addresses reliability and efficiency in power systems for utility providers, but it is incremental as it applies an existing method to a new domain.
The paper tackled anomaly detection in smart metering data by using a BiLSTM autoencoder to identify outliers, achieving classification based on reconstruction error thresholds tested on data from 985 households across four energy sources.
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.