Chapter
Jan 8, 2013
Artificial Neural Network for Controlling the Openings of Double Skin Envelopes and Cooling Systems
Authors: J. W. Moon [email protected], J. D. Chang [email protected], and S. Kim [email protected]Author Affiliations
Publication: ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction
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.
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© 2013 American Society of Civil Engineers.
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Published online: Jan 8, 2013
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Department of Building and Plant Engineering, Hanbat National University, S8-dong 4th Floor 125 Dongseodaero Yuseong-gu, Daejeon, South Korea 305-719. E-mail: [email protected]
School of Architecture, Design & Planning, University of Kansas, 1465 Jayhawk Blvd., Lawrence, KS 66045 USA. E-mail: [email protected]
Department of Housing and Interior Design, Yonsei University, 50 Yonseiro Seodaemunku, Seoul, 120-749 Korea. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.