Journal
BUILDING AND ENVIRONMENT
Volume 60, Issue -, Pages 243-253Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2012.10.013
Keywords
Building simulation; Monte Carlo; HVAC and mechanical systems; Demand controlled ventilation (DCV); Building operational strategies; Standards/guidelines/codes
Funding
- Energy Efficient Buildings Hub (EEB HUB), Philadelphia, PA
- US DOE through the Pennsylvania State University
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1055584] Funding Source: National Science Foundation
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A potential source of energy savings in buildings is demand controlled ventilation (DCV), or dynamic modulation of the ventilation rate based on current occupancy. The impact of DCV on indoor air quality (IAQ) has not been investigated over a large range of indoor air processes or under the revised ventilation rate procedure (VRP) introduced in ASHRAE Standard 62.1-2004, which reduced per-occupant rates and added a constant per-area rate. A transient, multi-contaminant model of an area-normalized US office was created, and best estimates for distributions of model inputs across the US office sector were developed and used in a six city Monte Carlo simulation of dynamic ventilation strategies, including DCV and morning flushes. DCV implementation had a very minor effect on concentrations of ozone, particles, and carbon dioxide. The greatest effect was on daytime mean and peak concentration of total volatile organic compounds (TVOC). TVOC daytime means increased by 7-10% and peaks increased by 10-14%, depending on city. Adding a medium intensity morning flush to DCV almost completely mitigated the increase in mean concentration and reduced the peak concentration below the fixed ventilation baseline in most cases. Differences among offices due to input variations were far greater than changes observed from implementing DCV, and a sensitivity analysis indicated that the TVOC emission rate was more influential than the ventilation strategy. The distribution-based, sector-wide Monte Carlo method developed here should also be useful for assessing other ventilation strategies and input parameter impacts and informing the development of IAQ guidelines. (c) 2012 Published by Elsevier Ltd.
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