Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42% annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.

Buildings account for 40% of US primary energy consumption and 72% of electricity. Of this total, 50% of the energy consumed in buildings is used for Heating Ventilation and Air-Conditioning (HVAC) systems. Current HVAC systems only condition based on static schedules; rooms are conditioned regardless of occupancy. By conditioning rooms only when necessary, greater efficiency can be achieved. This paper describes POEM, a complete closed-loop system for optimally controlling HVAC systems in buildings based on actual occupancy levels. POEM is comprised of multiple parts. A wireless network of cameras called OPTNet is developed that functions as an optical turnstile to measure area/zone occupancies. Another wireless sensor network of passive infrared (PIR) sensors called BONet functions alongside OPTNet. This sensed occupancy data from both systems are then fused with an occupancy prediction model using a particle filter in order to determine the most accurate current occupancy in each zone in the building. Finally, the information from occupancy prediction models and current occupancy is combined in order to find the optimal conditioning strategy required to reach target temperatures and minimize ventilation requirements. Based on live tests of the system, we estimate ~30.0% energy saving can be achieved while still maintaining thermal comfort.

Buildings are often inefficiently conditioned. Rooms that are empty are needlessly conditioned and partially filled rooms are conditioned assuming maximum occupancy. In this demonstration, we describe a system that reduces energy consumption by opportunistically reducing energy consumption based on room usage; we only condition rooms currently occupied and condition the space based on real-time occupancy measurements. We will show how a thermal sensor array can be used measure occupancy in real-time and how this occupancy information can be integrated with a real building management system in order to control the heating, cooling, ventilation and lighting of a building to optimize energy usage.

Running efficiency is an important factor to consider in order to avoid injury. In particular, foot pronation, the angle of the foot as it hits the ground, is a common cause for many types of injuries among runners. Though pronation is common, diagnosing pronation is difficult and imprecise. Currently there is no method of diagonsis which can quantify the severity of pronation. In this paper we propose using WISP (Wireless Identification and Sensing Platform) sensors to help identify and quantify foot pronation.