TY - GEN
T1 - Enhanced building thermal model by using CO2 based occupancy data
AU - Imanishi, Tomoya
AU - Tennekoon, Rajitha
AU - Palensky, Peter
AU - Nishi, Hiroaki
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - Prevailing low energy buildings attracts lots of attention in the world. Many studies have contributed in introducing higher thermal efficiency towards rooms with low energy heating, ventilation, and air-conditioning (HVAC) systems. However, current HVAC systems do not consider CO2 concentration change and thermal contribution towards human bodies in a room. This paper presents a novel method to predict thermal dynamics, including person count. Occupancy data are dynamically estimated by CO2 concentration and thermal contribution from the human bodies. The model is formulated as a resistor-capacitor circuit (RC circuit) in the Modelica modeling language. All parameters in a simulation are identified using actual building data during the winter season in Japan. Results are validated using measured information of actual building environment, and the test results concluded an improvement of absolute percentage error by 0.16 % over the conventional model. From the test results, it was concluded that the moving average filter of 20 minutes was an appropriate mean time to represent the time delay value.
AB - Prevailing low energy buildings attracts lots of attention in the world. Many studies have contributed in introducing higher thermal efficiency towards rooms with low energy heating, ventilation, and air-conditioning (HVAC) systems. However, current HVAC systems do not consider CO2 concentration change and thermal contribution towards human bodies in a room. This paper presents a novel method to predict thermal dynamics, including person count. Occupancy data are dynamically estimated by CO2 concentration and thermal contribution from the human bodies. The model is formulated as a resistor-capacitor circuit (RC circuit) in the Modelica modeling language. All parameters in a simulation are identified using actual building data during the winter season in Japan. Results are validated using measured information of actual building environment, and the test results concluded an improvement of absolute percentage error by 0.16 % over the conventional model. From the test results, it was concluded that the moving average filter of 20 minutes was an appropriate mean time to represent the time delay value.
KW - Building simulation
KW - CO2 concentration
KW - Modelica
KW - Occupancy estimation
KW - Thermal modeling
UR - http://www.scopus.com/inward/record.url?scp=84973120157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973120157&partnerID=8YFLogxK
U2 - 10.1109/IECON.2015.7392578
DO - 10.1109/IECON.2015.7392578
M3 - Conference contribution
AN - SCOPUS:84973120157
T3 - IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society
SP - 3116
EP - 3121
BT - IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015
Y2 - 9 November 2015 through 12 November 2015
ER -