  Leveraging data collected from smart meters in buildings can aid in
developing policies towards energy conservation. Significant energy savings
could be realised if deviations in the building operating conditions are
detected early, and appropriate measures are taken. Towards this end, machine
learning techniques can be used to automate the discovery of these abnormal
patterns in the collected data. Current methods in anomaly detection rely on an
underlying model to capture the usual or acceptable operating behaviour. In
this paper, we propose a novel attention mechanism to model the consumption
behaviour of a building and demonstrate the effectiveness of the model in
capturing the relations using sample case studies. A real-world dataset is
modelled using the proposed architecture, and the results are presented. A
visualisation approach towards understanding the relations captured by the
model is also presented.
