Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network

Jin Woo Moon, Ji Hyun Lee, Younju Yoon, Sooyoung Kim

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

This study aims to develop an artificial neural network (ANN)-based temperature control method to keep energy efficient indoor thermal environment in buildings with double skin envelope systems. Control logic that effectively controls the opening conditions of air inlets and outlets of the double skin envelope as well as the operation of the cooling system was developed employing the ANN model. To determine the optimal structure and learning methods for the ANN model, 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; this process was followed by performance tests of various optimized models. Analysis of the performance tests proved predictability and adaptability of the developed ANN model for diverse background conditions in terms of stable root mean square (RMS) and mean square error (MSE) values. The developed ANN model showed strong potential as a temperature control method for indoor thermal environment of buildings with double skin envelope systems.

Original languageEnglish
Pages (from-to)175-183
Number of pages9
JournalEnergy and Buildings
Volume69
DOIs
Publication statusPublished - 2014 Feb 1

Fingerprint

Skin
Neural networks
Temperature control
Air intakes
Cooling systems
Mean square error
Neurons
Hot Temperature

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this

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abstract = "This study aims to develop an artificial neural network (ANN)-based temperature control method to keep energy efficient indoor thermal environment in buildings with double skin envelope systems. Control logic that effectively controls the opening conditions of air inlets and outlets of the double skin envelope as well as the operation of the cooling system was developed employing the ANN model. To determine the optimal structure and learning methods for the ANN model, 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; this process was followed by performance tests of various optimized models. Analysis of the performance tests proved predictability and adaptability of the developed ANN model for diverse background conditions in terms of stable root mean square (RMS) and mean square error (MSE) values. The developed ANN model showed strong potential as a temperature control method for indoor thermal environment of buildings with double skin envelope systems.",
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Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network. / Moon, Jin Woo; Lee, Ji Hyun; Yoon, Younju; Kim, Sooyoung.

In: Energy and Buildings, Vol. 69, 01.02.2014, p. 175-183.

Research output: Contribution to journalArticle

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