Improving building energy efficiency with a network of sensing, learning and prediction agents

Abstract

Nearly 20 % of total energy consumption in the United States is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management agents can greatly decrease the energy usage of HVAC systems in many building applications, for example by enabling the operator to shut off HVAC to unoccupied rooms. We implement a multi-modal sensor agent that is non-intrusive and low-cost, combining information such as motion detection, CO2 reading, sound level, ambient light, and door state sensing. We show that in our live testbed at the USC campus, these sensor agents can be used to accurately estimate the number of occupants in each room using machine learning techniques, and that these techniques can also be applied to predict future occupancy by creating agent models of the occupants. These predictions will be used by control agents to enable the HVAC system increase its efficiency by continuously adapting to occupancy forecasts of each room.