Artificial neural network for controlling the openings of double skin envelopes and cooling systems

J. W. Moon, J. D. Chang, S. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This study is aimed at developing an artificial neural network (ANN)-based temperature control method for buildings with a double skin envelope. For this objective, logic for controlling the opening conditions of inlets and outlets of the double facade as well as the cooling system's operation was developed employing the ANN model for predictive and adaptive controls. For the optimal ANN model's structure and learning methods, a parametrical optimization process was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment followed by the performance tests of this optimized model. Analysis of the performance tests proved predictability and adaptability of the developed ANN model for diverse background conditions in terms of a stable Root Mean Square and Mean Square Error values. Results of the study indicated that the developed ANN model could potentially be applied to control temperature of double skin envelope buildings.

Original languageEnglish
Title of host publicationICSDEC 2012
Subtitle of host publicationDeveloping the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction
Pages81-89
Number of pages9
DOIs
Publication statusPublished - 2013 Nov 15
Event2nd Annual International Conference Sustainable Design, Engineering and Construction, ICSDEC 2012 - Fort Worth, TX, United States
Duration: 2012 Nov 72012 Nov 9

Other

Other2nd Annual International Conference Sustainable Design, Engineering and Construction, ICSDEC 2012
CountryUnited States
CityFort Worth, TX
Period12/11/712/11/9

Fingerprint

Cooling systems
Skin
Neural networks
Temperature control
Facades
Model structures
Mean square error
Neurons

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Architecture

Cite this

Moon, J. W., Chang, J. D., & Kim, S. (2013). Artificial neural network for controlling the openings of double skin envelopes and cooling systems. In ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction (pp. 81-89) https://doi.org/10.1061/9780784412688.010
Moon, J. W. ; Chang, J. D. ; Kim, S. / Artificial neural network for controlling the openings of double skin envelopes and cooling systems. ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction. 2013. pp. 81-89
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Moon, JW, Chang, JD & Kim, S 2013, Artificial neural network for controlling the openings of double skin envelopes and cooling systems. in ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction. pp. 81-89, 2nd Annual International Conference Sustainable Design, Engineering and Construction, ICSDEC 2012, Fort Worth, TX, United States, 12/11/7. https://doi.org/10.1061/9780784412688.010

Artificial neural network for controlling the openings of double skin envelopes and cooling systems. / Moon, J. W.; Chang, J. D.; Kim, S.

ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction. 2013. p. 81-89.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Moon JW, Chang JD, Kim S. Artificial neural network for controlling the openings of double skin envelopes and cooling systems. In ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction - Proceedings of the 2012 International Conference on Sustainable Design and Construction. 2013. p. 81-89 https://doi.org/10.1061/9780784412688.010