Determining Infiltration Rates and Predicting Building Occupancy Using CO2 Concentration Curves

Abstract

Demand controlled ventilation (DCV) reduces energy loss by reducing the air exchange flow rate to the minimum required to maintain acceptable indoor air quality (IAQ). DCV commonly uses carbon dioxide (CO2) as a proxy for human activity and increases the ventilation rate once a preset CO2 threshold is exceeded. Significant improvements over threshold based ODV strategies are possible if the natural infiltration rate of the building is measured and the occupancy schedule determined by analysing the CO2 concentration continuously. These calculated parameters allow mathematical modeling of the ventilated space and the determination of future CO2 concentrations and allow prediction of future ventilation demands. The natural infiltration rate and the onset and duration of vacancy periods in a residential dwelling were determined by analysing CO2 concentration data. Concentration declines which fit an exponential decay curve with a correlation coefficient >0.90 identified all vacancy periods. The measured natural infiltration rate was found statistically correlated with average wind speed. A dynamic predicted occupancy map was constructed that has the potential to facilitate significant energy savings via deferred ventilation and intelligent cooling and heating strategies.