Estimating building airflow using CO₂ measurements from a distributed sensor network

An accurate estimate of airflow rates within a building, namely the infiltration and interzonal airflow, is needed when determining energy use and indoor air quality and for detecting contaminants. The objective of this article is to demonstrate the feasibility of estimating airflow in a commercial building using carbon dioxide (CO₂) as a tracer. In this article, development of the proposed building airflow network inverse models (both deterministic and stochastic) is presented along with the methods used to test their performance. It is shown that the proposed inverse models would be able to determine the airflow for a one-story building of any size, as long as the rank of the known-information matrix is greater than or equal to the number of unknown airflow rates. Synthetic perfect and imperfect steady-state and transient CO₂ measurements are used to test the performance of the proposed building airflow network inverse models.