Abstract
This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces' openings as well as for cooling system's operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.
| Original language | English |
|---|---|
| Title of host publication | Progress in Environmental Science and Engineering |
| Pages | 2859-2865 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2013 Jan 8 |
| Event | 2nd International Conference on Energy, Environment and Sustainable Development, EESD 2012 - Jilin, China Duration: 2012 Oct 12 → 2012 Oct 14 |
Publication series
| Name | Advanced Materials Research |
|---|---|
| Volume | 610-613 |
| ISSN (Print) | 1022-6680 |
Other
| Other | 2nd International Conference on Energy, Environment and Sustainable Development, EESD 2012 |
|---|---|
| Country | China |
| City | Jilin |
| Period | 12/10/12 → 12/10/14 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Artificial neural network for the control of the openings and cooling systems of the double skin envelope buildings. / Moon, Jin Woo; Kim, Sooyoung.
Progress in Environmental Science and Engineering. 2013. p. 2859-2865 (Advanced Materials Research; Vol. 610-613).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Artificial neural network for the control of the openings and cooling systems of the double skin envelope buildings
AU - Moon, Jin Woo
AU - Kim, Sooyoung
PY - 2013/1/8
Y1 - 2013/1/8
N2 - This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces' openings as well as for cooling system's operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.
AB - This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces' openings as well as for cooling system's operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.
UR - http://www.scopus.com/inward/record.url?scp=84871873590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871873590&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMR.610-613.2859
DO - 10.4028/www.scientific.net/AMR.610-613.2859
M3 - Conference contribution
SN - 9783037855508
T3 - Advanced Materials Research
SP - 2859
EP - 2865
BT - Progress in Environmental Science and Engineering
ER -