A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System

arXiv:2405.02574v1 Announce Type: new
Abstract: 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 …

abstract advance anomaly anomaly detection arxiv building buildings commercial consumption cs.lg data data mining detection emissions global mining monitoring operations power power consumption total type unsupervised work