Abstract
Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently, other energy sources such as water, gas, and heating have also been actively collected. As a result, it is necessary to develop a unified methodology for anomaly detection across multiple energy sources; however, research efforts have rarely been made to tackle this issue. In this study, we propose a new correlation-driven multi-level learning model for anomaly detection on multiple energy sources. The distinguishing property of the model incorporates multiple energy sources in multi-levels based on the strength of the correlations between them. Our model is scalable to integrate arbitrary new energy sources, with further performance improvement, considering both correlated and non-correlated sources. Through extensive experiments on real-world datasets consisting of three to five energy sources, we demonstrate that the proposed model clearly outperforms the existing multimodal learning and recent time-series anomaly detection models and makes further performance improvement as more energy sources are integrated, showing the scalability of the proposed model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have