Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution

Ahmad, J.; Larijani, H.; Emmanuel, R.; Mannion, M.; Javed, A.

Abstract

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.

Citation

Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., & Javed, A. (2018). Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution. Applied Computing and Informatics, https://doi.org/10.1016/j.aci.2018.12.001

• Journal Article Type: Article
• Acceptance Date: Dec 2, 2018
• Online Publication Date: Dec 7, 2018
• Publication Date: 2018-12
• Deposit Date: Sep 16, 2019
• Publicly Available Date: Sep 16, 2019
• Journal: Applied Computing and Informatics
• Print ISSN: 2210-8327
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.aci.2018.12.001
• Keywords: Buildings; Occupancy; Energy optimization; HVAC
• Public URL: http://researchrepository.napier.ac.uk/Output/2133607