Abstract
Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.
| Original language | English |
|---|---|
| Title of host publication | 2013 10th IEEE International Conference on Control and Automation, ICCA 2013 |
| Pages | 1896-1901 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 10th IEEE International Conference on Control and Automation, ICCA 2013 - Hangzhou, China Duration: Jun 12 2013 → Jun 14 2013 |
Other
| Other | 2013 10th IEEE International Conference on Control and Automation, ICCA 2013 |
|---|---|
| Country | China |
| City | Hangzhou |
| Period | 6/12/13 → 6/14/13 |
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Keywords
- Artificial neural network
- HVAC
- Model predictive control
- Multi-zone
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
Cite this
Multi-zone temperature prediction in a commercial building using artificial neural network model. / Huang, Hao; Chen, Lei; Mohammadzaheri, Morteza; Hu, Eric; Chen, Minlei.
2013 10th IEEE International Conference on Control and Automation, ICCA 2013. 2013. p. 1896-1901 6565010.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multi-zone temperature prediction in a commercial building using artificial neural network model
AU - Huang, Hao
AU - Chen, Lei
AU - Mohammadzaheri, Morteza
AU - Hu, Eric
AU - Chen, Minlei
PY - 2013
Y1 - 2013
N2 - Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.
AB - Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.
KW - Artificial neural network
KW - HVAC
KW - Model predictive control
KW - Multi-zone
UR - http://www.scopus.com/inward/record.url?scp=84882321303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882321303&partnerID=8YFLogxK
U2 - 10.1109/ICCA.2013.6565010
DO - 10.1109/ICCA.2013.6565010
M3 - Conference contribution
AN - SCOPUS:84882321303
SN - 9781467347075
SP - 1896
EP - 1901
BT - 2013 10th IEEE International Conference on Control and Automation, ICCA 2013
ER -