Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources. (arXiv:2305.02323v1 [cs.LG])

Advanced metering infrastructure (AMI) has been widely used as an intelligent
energy consumption measurement system. Electric power was the representative
energy source that can be 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. The inherent
difficulty with this issue stems from the fact that anomalies are not usually
annotated. Moreover, existing works of anomaly definition depend on only
individual energy sources. In this paper, we first propose a method for
defining anomalies considering not only individual energy sources but also
correlations between them. Then, we propose a new Correlation-driven
Multi-Level Multimodal 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 strengths of the correlations between
them. Furthermore, we generalize the proposed model in order to integrate
arbitrary new energy sources with further performance improvement, considering
not only correlated but also 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 we
observe that our model makes further the performance improvement as more
correlated or non-correlated energy sources are integrated.