Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings

Development of a thermal control algorithm using artificial neural network models for improved... Applied Thermal Engineering 103 (2016) 1135–1144 Contents lists available at ScienceDirect Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng Research Paper Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings a b,⇑ Jin Woo Moon , Sung Kwon Jung School of Architecture and Building Science, Chung-Ang University, Seoul, South Korea Department of Architectural Engineering, Dankook University, Yongin-si, South Korea highl i ghts graphical a bstrac t An ANN model for predicting optimal start moment of the cooling system was developed. An ANN model for predicting the amount of cooling energy consumption was developed. An optimal control algorithm was developed employing two ANN models. The algorithm showed the advanced thermal comfort and energy efficiency. article i nfo abstract Article history: The aim of this study was to develop a control algorithm to demonstrate the improved thermal comfort Received 20 October 2015 and building energy efficiency of accommodation buildings in the cooling season. For this, two artificial Revised 15 February 2016 neural network (ANN)-based predictive and adaptive models were developed and employed in the algo- Accepted 1 May 2016 rithm. One model predicted the cooling energy consumption during the unoccupied period http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Thermal Engineering Elsevier

Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
1359-4311
eISSN
1873-5606
D.O.I.
10.1016/j.applthermaleng.2016.05.002
Publisher site
See Article on Publisher Site

Abstract

Applied Thermal Engineering 103 (2016) 1135–1144 Contents lists available at ScienceDirect Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng Research Paper Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings a b,⇑ Jin Woo Moon , Sung Kwon Jung School of Architecture and Building Science, Chung-Ang University, Seoul, South Korea Department of Architectural Engineering, Dankook University, Yongin-si, South Korea highl i ghts graphical a bstrac t An ANN model for predicting optimal start moment of the cooling system was developed. An ANN model for predicting the amount of cooling energy consumption was developed. An optimal control algorithm was developed employing two ANN models. The algorithm showed the advanced thermal comfort and energy efficiency. article i nfo abstract Article history: The aim of this study was to develop a control algorithm to demonstrate the improved thermal comfort Received 20 October 2015 and building energy efficiency of accommodation buildings in the cooling season. For this, two artificial Revised 15 February 2016 neural network (ANN)-based predictive and adaptive models were developed and employed in the algo- Accepted 1 May 2016 rithm. One model predicted the cooling energy consumption during the unoccupied period

Journal

Applied Thermal EngineeringElsevier

Published: Jun 25, 2016

References

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