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
Applied Thermal Engineering – Elsevier
Published: Jun 25, 2016
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