Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems

Article


Rana, Rajib, Kusy, Brano, Wall, Josh and Hu, Wen. 2015. "Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems." Energy. 93 (1), pp. 245-255. https://doi.org/10.1016/j.energy.2015.09.002
Article Title

Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems

ERA Journal ID5115
Article CategoryArticle
AuthorsRana, Rajib (Author), Kusy, Brano (Author), Wall, Josh (Author) and Hu, Wen (Author)
Journal TitleEnergy
Journal Citation93 (1), pp. 245-255
Number of Pages11
Year2015
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0360-5442
1873-6785
Digital Object Identifier (DOI)https://doi.org/10.1016/j.energy.2015.09.002
Web Address (URL)http://www.sciencedirect.com/science/article/pii/S0360544215011883
Abstract

Reductions in HVAC (heating, ventilation and air conditioning) energy consumption can be achieved by limiting heating in the winter or cooling in the summer. However, the resulting low thermal comfort of building occupants may lead to an override of the HVAC control, which revokes its original purpose. This has led to an increased interest in modeling and real-time tracking of location, activity, and thermal comfort of building occupants for HVAC energy management. While thermal comfort is well understood, it is difficult to measure in real-time environments where user context changes dynamically. Encouragingly, plethora of sensors available on smartphone unleashes the opportunity to measure user contexts in real-time. An important contextual information for measuring thermal comfort is Metabolism rate, which changes based on current physical activities. To measure physical activity, we develop an activity classifier, which achieves 10% higher accuracy compared to Support Vector Machine and k-Nearest Neighbor. Office occupancy is another contextual information for energy-efficient HVAC control. Most of the phone based occupancy estimation techniques will fail to determine occupancy when phones are left at desk while sitting or attending meetings. We propose a novel sensor fusion method to detect if a user is near the phone, which achieves more than 90% accuracy. Determining activity and occupancy our proposed algorithms can help maintaining thermal comfort while reducing HVAC energy consumptions.

KeywordsHVAC (heating, ventilation and air conditioning); sparse random classifier; sensor fusion; smartphone; occupancy; physical activity
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
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Byline AffiliationsDeputy Vice-Chancellor's Office (Research and Innovation)
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Institution of OriginUniversity of Southern Queensland
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