Smart Oracle Based Building Management System

Buildings in residential and commercial sites consume close to 40 per cent of the world's total energy produced and is growing at a steady pace. The need to lower the energy footprint is a matter of sustainability and active research for the smart building community. Recent trends in machine learning have led to significant work on occupancy detection in spaces by training isolated or ex-situ models, but with no reliability of performance on unknown spaces. Model applicability becomes questionable when the sensor value distribution is different from training data and in a real-life this is usually the case. Furthermore, analyzing a space on a floor-plan in silo obscures the holistic view of interactivity between building elements. In this paper, we propose the design of a generic building management system that auto-learns occupancy patterns and leverages spatial organization to deliver actionable insights on energy savings. We combine the building information with sensor signals into a Spatio-temporal activity graph, whose edges are dynamically updated based on occupancy. We introduce human-space interaction models to infer the human transmission capacity of each edge and compute an Eigenvalue score for all the spaces to derive automated checkpoints on space-wise appliance monitoring.