DocumentCode :
3218450
Title :
Real-time building occupancy sensing using neural-network based sensor network
Author :
Ekwevugbe, Tobore ; Brown, Niquelle ; Pakka, Vijay ; Fan, Deliang
Author_Institution :
Inst. of Energy & Sustainable Dev., De Montfort Univ. Leicester, Leicester, UK
fYear :
2013
fDate :
24-26 July 2013
Firstpage :
114
Lastpage :
119
Abstract :
Current occupancy sensing technologies may limit the effectiveness of buildings controls, due to a number of issues ranging from unreliable data, sensor drift, privacy concerns, and insufficient commissioning. More effective control of Heating, Ventilation and Air-conditioning (HVAC) systems may be possible using a smart and adaptive sensing network for occupancy detection, capable of turning off services out of hours, and not over-ventilating, thus enabling energy savings, and not under-ventilating during occupied periods, giving comfort and health benefits. A low-cost and non-intrusive sensor network was deployed in an open-plan office, combining information such as sound level, case temperature, carbon-dioxide (Co2) and motion, to estimate occupancy numbers, while an infrared camera was implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis was used for feature selection, and a genetic based search to evaluate an optimal sensor combination. Selected multi-sensory features were fused using a neural network. From initial results, estimation accuracy reaching up to 75% for occupied periods was achieved. The proposed system offers promising opportunities for improved comfort control and energy efficiency in buildings.
Keywords :
HVAC; building; learning (artificial intelligence); neural nets; HVAC systems; adaptive sensing network; energy efficiency; feature selection; genetic based search; heating ventilation and air-conditioning systems; improved comfort control; neural-network based sensor network; non intrusive sensor network; occupancy detection; optimal sensor combination; real-time building occupancy sensing; Accuracy; Buildings; Feature extraction; Predictive models; Temperature measurement; Temperature sensors; Energy savings; Features; HVAC systems; Occupancy; Sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Ecosystems and Technologies (DEST), 2013 7th IEEE International Conference on
Conference_Location :
Menlo Park, CA
ISSN :
2150-4938
Print_ISBN :
978-1-4799-0784-7
Type :
conf
DOI :
10.1109/DEST.2013.6611339
Filename :
6611339
Link To Document :
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