Model-free HVAC control using occupant feedback

Purdon, Sean, Kusy, Branislav K, Jurdak, Raja and Challen, Geoffrey (2013). Model-free HVAC control using occupant feedback. In: Damla Turgut, Nils Aschenbruck and Jens Tölle, Proceedings of the 38th Annual IEEE Conference on Local Computer Networks (LCN 2013): LCN Workshops. Second IEEE International Workshop on Global Trends in Smart Cities 2013, Sydney, Australia, (84-92). 21-24 October 2013. doi:10.1109/LCNW.2013.6758502

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Author Purdon, Sean
Kusy, Branislav K
Jurdak, Raja
Challen, Geoffrey
Title of paper Model-free HVAC control using occupant feedback
Conference name Second IEEE International Workshop on Global Trends in Smart Cities 2013
Conference location Sydney, Australia
Conference dates 21-24 October 2013
Proceedings title Proceedings of the 38th Annual IEEE Conference on Local Computer Networks (LCN 2013): LCN Workshops
Journal name Proceedings - Conference on Local Computer Networks, LCN
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/LCNW.2013.6758502
Open Access Status
ISBN 9781467305409
9781479905393
Editor Damla Turgut
Nils Aschenbruck
Jens Tölle
Start page 84
End page 92
Total pages 4
Language eng
Abstract/Summary Optimal control of Heating, Ventilation, and Air Conditioning (HVAC) is an important step towards reducing the carbon footprint of buildings and requires balancing energy reductions and occupant comfort. Conventional thermostats for temperature set points provide a centralised single point of user input, often leading to significant thermal discomfort for occupants. We propose to instead include users in the HVAC control loop through distributed smart-phone based votes about their thermal comfort for aggregated control of HVAC. Unlike existing approaches that require in-situ sensors or build complex comfort models of individual users, we propose a model- and sensor-free HVAC control algorithm that uses simple user input (hot/cold) and adapts to changing office occupancy or ambient temperature in real time. We develop an iterative data fusion algorithm that finds optimal temperature in offices with multiple users and propose techniques that can aggressively save energy by drifting indoor temperatures towards the outdoor temperature. Our evaluation is based on empirical data collected in 12 offices over a 3-week period and shows that adaptive HVAC control can save up to 60% of energy at a relatively small increase of 0.3°C in average occupant discomfort.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes The Second IEEE International Workshop on Global Trends in Smart Cities 2013 was held in conjunction with the 2013 IEEE 38th Conference on Local Computer Networks Workshops (LCN 2013).

 
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