Development of an artificial neural network model based thermal control logic for double skin envelopes in winter

Jin Woo Moon, Sung Hoon Yoon, Sooyoung Kim

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

This study proposes an Artificial Neural Network (ANN)-based thermal control method for double skin envelope buildings in winter. A thermal control logic for controlling heating systems and openings on the internal and external envelopes of a double skin building was developed using the ANN-based predictive and adaptive control model. Employing the predicted values for the future indoor air temperature (i.e., the air temperature rise or drop by the next control cycle), the control logic predetermines the operation of the heating system and the opening conditions of internal and external envelopes of a double skin building. After the parametrical optimization of the initial ANN model, the performance of the optimized ANN model was tested for prediction accuracy and adaptability using the data measured from an actual double-skinned envelope building. The analysis results revealed that the ANN model proved its prediction accuracy and adaptability for the different climate conditions and envelope orientations. The developed control logic and model in this study are effectively applied for thermal control of double skinned envelope buildings.

Original languageEnglish
Pages (from-to)149-159
Number of pages11
JournalBuilding and Environment
Volume61
DOIs
Publication statusPublished - 2013 Mar 1

Fingerprint

neural network
artificial neural network
skin
Skin
Neural networks
winter
air temperature
heat pump
heating
building
air
indoor air
prediction
climate conditions
Heating
Air
Hot Temperature
climate
Temperature
performance

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Geography, Planning and Development
  • Civil and Structural Engineering
  • Building and Construction

Cite this

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abstract = "This study proposes an Artificial Neural Network (ANN)-based thermal control method for double skin envelope buildings in winter. A thermal control logic for controlling heating systems and openings on the internal and external envelopes of a double skin building was developed using the ANN-based predictive and adaptive control model. Employing the predicted values for the future indoor air temperature (i.e., the air temperature rise or drop by the next control cycle), the control logic predetermines the operation of the heating system and the opening conditions of internal and external envelopes of a double skin building. After the parametrical optimization of the initial ANN model, the performance of the optimized ANN model was tested for prediction accuracy and adaptability using the data measured from an actual double-skinned envelope building. The analysis results revealed that the ANN model proved its prediction accuracy and adaptability for the different climate conditions and envelope orientations. The developed control logic and model in this study are effectively applied for thermal control of double skinned envelope buildings.",
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Development of an artificial neural network model based thermal control logic for double skin envelopes in winter. / Moon, Jin Woo; Yoon, Sung Hoon; Kim, Sooyoung.

In: Building and Environment, Vol. 61, 01.03.2013, p. 149-159.

Research output: Contribution to journalArticle

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