참고문헌
- R. Cantin, A. Kindinis, P. Michel. New approaches for overcoming the complexity of future buildings impacted by new energy constraints. Futures 2012; 44: 735-745. https://doi.org/10.1016/j.futures.2012.05.001
- L. Perez-Lombard, J. Ortiz, C. Pout. A review on buildings energy consumption information. Energy and Buildings 2008; 40: 394-398. https://doi.org/10.1016/j.enbuild.2007.03.007
-
M. Ng, M. Qu, P. Zheng, Z. Li, Y. Hang.
$CO_2$ -based demand controlled ventilation under new ASHRAE Standard 62.1-2010: a case study for a gymnasium of an elementary school at West Lafayette, Indiana. Energy and Buildings 2011; 43: 11, 3216-3225. https://doi.org/10.1016/j.enbuild.2011.08.021 - A. Rackes, M. Waring. Modeling impacts of dynamic ventilation strategies on indoor air quality of offices in six US cities. Building and Environment 2013; 60: 243-253. https://doi.org/10.1016/j.buildenv.2012.10.013
-
S. Emmerich, A. Persily. State-Of-The-Art Review of
$CO_2$ Demand Controlled Ventilation Technology and Application, NISTIR 6729. National Institute of Standards and technology (2001). -
X. Lin, J. Lau. Demand controlled ventilation for multiple zone HVAC systems:
$CO_2$ -based dynamic reset (RP 1547). HVAC&R Research 2014; 20: 8, 875-888. https://doi.org/10.1080/10789669.2014.945853 -
T. Lu, X. Lu, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy for
$CO_2$ control and energy saving in buildings. Energy and Buildings 2011; 43:9, 2499-2508. https://doi.org/10.1016/j.enbuild.2011.06.005 -
N. Nassif. A robust
$CO_2$ -based demand-controlled ventilation control strategy for multi-zone HVAC systems. Energy and Buildings 2012; 45: 72-81. https://doi.org/10.1016/j.enbuild.2011.10.018 -
Z. Sun, S. Wang, Z. Ma. In-situ implementation and validation of a
$CO_2$ -based adaptive demandcontrolled ventilation strategy in a multi-zone office building. Building and Environment 2011; 46: 1, 124-133. https://doi.org/10.1016/j.buildenv.2010.07.008 -
D. Cali, P. Matthes, K. Huchtemann, R. Streblow, D. Muller.
$CO_2$ based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings. Building and Environment 2015; 86: 39-49. https://doi.org/10.1016/j.buildenv.2014.12.011 - Demetriou, D. and H. Khalifa. Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings. Building Simulation 2009; 2:3, 197-214. https://doi.org/10.1007/s12273-009-9320-z
- T. Labeodan, W. Zeiler, G. Boxem, Y. Zhao. Occupancy measurement in commercial office buildings for demand-driven control applications-A survey and detection system evaluation. Energy and Buildings 2015; 93: 303-314. https://doi.org/10.1016/j.enbuild.2015.02.028
- M. Mysen, S. Berntsen, P. Nafstad, P. Schild. Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools. Energy and Buildings 2015; 37: 12, 1234-1240. https://doi.org/10.1016/j.enbuild.2005.01.003
- E. Naghiyev, M. Gillott, R. Wilson. Three unobtrusive domestic occupancy measurement technologies under qualitative review. Energy and Buildings 2014; 69: 507-514. https://doi.org/10.1016/j.enbuild.2013.11.033
-
S. Wang, J. Burnett, H. Chong. Experimental validation of
$CO_2$ - based occupancy detection for demand-controlled ventilation. Indoor Built Environment 1999; 8: 6, 377-391. https://doi.org/10.1177/1420326X9900800605 - IESNA 9th Edition Handbook, Illuminating Engineering Society of North America (2010).