  Building operations consume 30% of total power consumption and contribute 26%
of global power-related emissions. Therefore, monitoring, and early detection
of anomalies at the meter level are essential for residential and commercial
buildings. This work investigates both supervised and unsupervised approaches
and introduces a dynamic anomaly detection system. The system introduces a
supervised Light Gradient Boosting machine and an unsupervised autoencoder with
a dynamic threshold. This system is designed to provide real-time detection of
anomalies at the meter level. The proposed dynamical system comes with a
dynamic threshold based on the Mahalanobis distance and moving averages. This
approach allows the system to adapt to changes in the data distribution over
time. The effectiveness of the proposed system is evaluated using real-life
power consumption data collected from smart metering systems. This empirical
testing ensures that the system's performance is validated under real-world
conditions. By detecting unusual data movements and providing early warnings,
the proposed system contributes significantly to visual analytics and decision
science. Early detection of anomalies enables timely troubleshooting,
preventing financial losses and potential disasters such as fire incidents.
