Graphical models built on sensor networks have been used extensively in smart
home projects to improve occupancy comfort and building energy use. Simulation
tools use profiles of occupants to predict future building energy use. Previous
research focused on past or present occupancy and mobility. But the social interactions
are often ignored. In this study, we model occupancy activities by constructing
a social network from information provided by physical sensor networks
in an open-plan office building. We propsed a Time Series Maximum Margin
Markov Network model (TM3N) to incorporate information from evolving networks,
e.g., number of occupants, occupant activities and indoor and outdoor CO2
changes. We then constructed an energy simulation model of the building from
inference results. Simulation results show that energy savings reached 20% in the
demonstration building while maintaining indoor occupancy thermal comfort.
Xiaoqian Jiang, Bing Dong, and Latanya Sweeney. From Sensor Network To Social Network– A Study On The Energy Impact In Buildings. NIPS Workshop Analyzing Networks and Learning with Graphs, 2009. (PDF).