AAAR 33rd Annual Conference
October 20 - October 24, 2014
Rosen Shingle Creek
Orlando, Florida, USA
Abstract View
How Air Quality Metrics and Wireless Technology can Maximize the Energy Efficiency of HVAC in a Working Auditorium
ANNA LEAVEY, Yong Fu, Mo Sha, Andrew Kutta, Chenyang Lu, Wei-Ning Wang, Bill Drake, Yixin Chen, Pratim Biswas, Washington University in St Louis
Abstract Number: 344 Working Group: Indoor Aerosols
Abstract HVAC is the single largest consumer of energy in commercial and residential buildings. Reducing its energy consumption without compromising occupants’ comfort or indoor air quality would have environmental and financial benefits. This measurement study assesses the sources of indoor aerosols in a university auditorium, and evaluates whether a particle number metric could supplement the current CO$_2 and temperature sensors in informing occupancy times and subsequent HVAC control. A wireless testbed consisting of a retrofitted wireless Condensation particle counter (CPC), 25 wireless temperature sensors, 2 HVAC-embedded temperature and CO$_2 sensors, and a web camera was deployed in the working auditorium, to monitor the air quality, temperature, and occupancy of the room. The main objectives were to identify particle sources using the retrofitted CPC, map the temperature variability of the room and select the most optimally located sensors for HVAC control using clustering algorithms, and examine possible energy savings by operating the HVAC only during periods of occupancy using 1) calendar-based scheduling, and 2) air quality indicators, including a particle number metric, as proxies of occupancy. All air quality metrics increased with higher occupancy rates, although HVAC-modes changes were also identified as a source of particle numbers. Clustering analysis identified an alternative temperature sensor location for optimal HVAC control. Operating the HVAC using calendar-based scheduling resulted in energy savings of between 8 and 79%, increasing if occupancy events were scheduled close together. Finally, CO$_2 was the strongest predictor of occupancy counts with an R$^2 of 0.62 (p < 0.001) during simple regression analysis. Incorporating particle numbers and temperature improved estimates of occupancy only slightly (R$^2 = 0.67), however incorporating a particle metric may enable the general air quality to be monitored, and identify when filters should be replaced.