| dc.contributor.author |
Lawrence, TM |
en |
| dc.contributor.author |
Braun, JE |
en |
| dc.date.accessioned |
2014-06-06T06:47:20Z |
|
| dc.date.available |
2014-06-06T06:47:20Z |
|
| dc.date.issued |
2007 |
en |
| dc.identifier.issn |
03601323 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.buildenv.2004.10.021 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3529 |
|
| dc.subject |
Building occupancy |
en |
| dc.subject |
Carbon dioxide |
en |
| dc.subject |
CFD |
en |
| dc.subject |
Demand control ventilation |
en |
| dc.subject |
Energy simulation |
en |
| dc.subject |
Modeling |
en |
| dc.subject.other |
Air conditioning |
en |
| dc.subject.other |
Carbon dioxide |
en |
| dc.subject.other |
Computational fluid dynamics |
en |
| dc.subject.other |
Computer simulation |
en |
| dc.subject.other |
Heating |
en |
| dc.subject.other |
Parameter estimation |
en |
| dc.subject.other |
Ventilation |
en |
| dc.subject.other |
Building occupancy |
en |
| dc.subject.other |
Carbon dioxide source generation rates |
en |
| dc.subject.other |
Demand control ventilation |
en |
| dc.subject.other |
Indoor air pollution |
en |
| dc.subject.other |
Air conditioning |
en |
| dc.subject.other |
Carbon dioxide |
en |
| dc.subject.other |
Computational fluid dynamics |
en |
| dc.subject.other |
Computer simulation |
en |
| dc.subject.other |
Heating |
en |
| dc.subject.other |
Indoor air pollution |
en |
| dc.subject.other |
Parameter estimation |
en |
| dc.subject.other |
Ventilation |
en |
| dc.subject.other |
building |
en |
| dc.subject.other |
carbon dioxide |
en |
| dc.subject.other |
computational fluid dynamics |
en |
| dc.subject.other |
ventilation |
en |
| dc.title |
A methodology for estimating occupant CO2 source generation rates from measurements in small commercial buildings |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.buildenv.2004.10.021 |
en |
| heal.publicationDate |
2007 |
en |
| heal.abstract |
It is necessary to know CO2 source generation rates and system flow parameters, such as supply flow rate and overall room ventilation effectiveness, in order to evaluate cost savings for demand-controlled ventilation applied to commercial buildings. This paper presents a methodology for estimating schedules for generation rates and flow parameters using short-term testing. These parameters are used within a model that predicts return air CO2 concentrations as part of an overall energy analysis model. As a first step in developing the methodology, two different parameter estimation techniques were evaluated using simulated data. Each method gave models that provide good predictions of return air CO2 concentrations, but differed in terms of the identified parameters. The preferred parameter estimation method provides estimates of both average hourly source generation rates and day-to-day variations. This technique was applied to three different types of commercial buildings using field monitored data. The sites are small commercial buildings with packaged HVAC equipment and included modular schoolrooms, children's play areas in fast food restaurants and a pharmacy retail store. The impact of the length of model training data period on estimated CO2 generation rates was investigated. Eight weeks of data is sufficient for training. Expressed in terms of the coefficient of variation, the errors in predicted CO2 concentrations ranged from 4% to 15% depending on the sites. The predicted frequency of time that CO2 concentrations were within a given range agreed well with the field measured data. © 2005 Elsevier Ltd. All rights reserved. |
en |
| heal.journalName |
Building and Environment |
en |
| dc.identifier.issue |
2 |
en |
| dc.identifier.volume |
42 |
en |
| dc.identifier.doi |
10.1016/j.buildenv.2004.10.021 |
en |
| dc.identifier.spage |
623 |
en |
| dc.identifier.epage |
639 |
en |