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

TL;DR

This paper introduces a bi-directional LSTM autoencoder-based method for detecting anomalies in smart metering data, improving system reliability by accurately identifying outliers across multiple energy sources.

Contribution

It proposes a novel anomaly detection approach using BiLSTM autoencoders and demonstrates its effectiveness on real-world multi-source metering data.

Findings

Effective detection of anomalies in smart metering data

High accuracy across different energy sources

Potential for enhancing power system reliability

Abstract

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.