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Leveraging data collected from smart meters in buildings can aid indeveloping policies towards energy conservation. Significant energy savingscould be realised if deviations in the building operating conditions aredetected early, and appropriate measures are taken. Towards this end, machinelearning techniques can be used to automate the discovery of these abnormalpatterns in the collected data. Current methods in anomaly detection rely on anunderlying model to capture the usual or acceptable operating behaviour. Inthis paper, we propose a novel attention mechanism to model the consumptionbehaviour of a building and demonstrate the effectiveness of the model incapturing the relations using sample case studies. A real-world dataset ismodelled using the proposed architecture, and the results are presented. Avisualisation approach towards understanding the relations captured by themodel is also presented.

Attention Boosted Autoencoder for Building Energy Anomaly Detection