A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
This addresses the need for reliable anomaly detection in smart metering to prevent financial losses and disasters for organizations and consumers, though it appears incremental as it builds on existing autoencoder and statistical methods.
The paper tackles the problem of anomaly detection in real-time smart metering systems for energy consumption by introducing a hybrid approach combining a Convolutional Autoencoder with a dynamic threshold based on Mahalanobis distance and moving averages, achieving a real-time, meter-level detection system that provides early warnings.
The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring.