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Realt-Time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations
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Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for Heating, Ventilation and Air-conditioning (HVAC) systems. However, a precise and reliable measurement of occupancy still remains difficult. Existing technologies are plagued with a number of issues ranging from unreliable data, maintaining privacy and sensor drift. More effective control of HVAC systems may be possible using a smart sensing network for occupancy detection. A low-cost and non-intrusive sensor network is deployed in an open-plan office, combining information such as sound level and motion, to estimate occupancy numbers, while an infrared camera is implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis is used for feature selection, and selected multi-sensory features are fused using a neuralnetwork model, with occupancy estimation accuracy reaching up to 84.59%. The proposed system offers promising opportunities for reliable occupancy sensing, capable of supporting demand driven HVAC operations.
Ekwevugbe, T.; Brown, N.; Pakka, V.; (2013). Realt-Time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from