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Realt-Time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations
Abstract
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.
Citation
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 https : / /hdl .handle .net /1969 .1 /151431.