Korean Journal of Air-Conditioning and Refrigeration Engineering (설비공학논문집)

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
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Application of Artificial Neural Network for Optimum Controls of Windows and Heating Systems of Double-Skinned Buildings

이중외피 건물의 개구부 및 난방설비 제어를 위한 인공지능망의 적용

  • Moon, Jin-Woo (Department of Building and Plant Engineering, Hanbat National University) ;
  • Kim, Sang-Min (Research and Development Division, Hyundai Eng. and Construction Co., Ltd.) ;
  • Kim, Soo-Young (Department. of Housing and Interior Design, Yonsei University)
  • 문진우 (한밭대학교 설비공학과) ;
  • 김상민 (현대건설 연구개발본부) ;
  • 김수영 (연세대학교 주거환경학과)
  • Received : 2012.01.26
  • Published : 2012.08.10
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Abstract

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

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References

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  1. Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings vol.6, pp.8, 2013, https://doi.org/10.3390/en6084223