Mining anomalous electricity consumption using Ensemble Empirical Mode Decomposition
Romain Fontugne, Nicolas Tremblay, Pierre Borgnat, Patrick Flandrin, Hiroshi Esaki · 2013
Sensor deployments in large buildings allow the administrators to supervise the building infrastructure and identify abnormalities. Nevertheless, the numerous data streams reported by the increasing number of sensors overwhelm the building administrators. We propose a methodology that assists them to identify abnormal devices usages. The proposed method takes advantage of Ensemble Empirical Mode Decomposition (E-EMD) to uncover the patterns of power-draw signals, thereby enabling us to estimate the intrinsic inter-device correlations. By monitoring the devices correlations over time we compute the usual usage of the devices and report the devices that deviate from their normal usage. Our evaluation with 10 weeks of real data shows the efficiency of the proposed method to uncover the devices intrinsic relationships and detect peculiar events that require the administrators attention.