Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3116-3121 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479917624 |
| DOIs | |
| Publication status | Published - 2016 Jan 25 |
| Event | 41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015 - Yokohama, Japan Duration: 2015 Nov 9 → 2015 Nov 12 |
Other
| Other | 41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015 |
|---|---|
| Country | Japan |
| City | Yokohama |
| Period | 15/11/9 → 15/11/12 |
Fingerprint
Keywords
- Building simulation
- CO2 concentration
- Modelica
- Occupancy estimation
- Thermal modeling
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
Cite this
Enhanced building thermal model by using CO2 based occupancy data. / Imanishi, Tomoya; Tennekoon, Rajitha; Palensky, Peter; Nishi, Hiroaki.
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3116-3121 7392578.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
PY - 2016/1/25
Y1 - 2016/1/25
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
SP - 3116
EP - 3121
BT - IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society
PB - Institute of Electrical and Electronics Engineers Inc.
ER -