Collective contextual anomaly detection framework for smart buildings
Daniel B. Araya, Katarina Grolinger, Hany F. ElYamany, Miriam A. M. Capretz, Girma Bitsuamlak · 2016
Buildings are responsible for a significant amount of total global energy consumption and as a result account for a substantial portion of overall carbon emissions. Moreover, buildings have a great potential for helping to meet energy efficiency targets. Hence, energy saving goals that target buildings can have a significant contribution in reducing environmental impact. Today's smart buildings achieve energy efficiency by monitoring energy usage with the aim of detecting and diagnosing abnormal energy consumption behaviour. This research proposes a generic collective contextual anomaly detection (CCAD) framework that uses sliding window approach and integrates historic sensor data along with generated and contextual features to train an autoencoder to recognize normal consumption patterns. Subsequently, by determining a threshold that optimizes sensitivity and specificity, the framework identifies abnormal consumption behaviour. The research compares two models trained with different features using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.