Buildings account for 40% of US primary energy consump- tion 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 condi- tioned 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 de- veloped that functions as an optical turnstile to measure area/zone occupancies. Another wireless sensor network of passive infrared (PIR) sensors called BONet functions along- side OPTNet. This sensed occupancy data from both sys- tems are then fused with an occupancy prediction model us- ing 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 condi- tioning 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.
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