  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.
