KIEAE Journal

Korea Institute of Ecological Architecture and Environment (한국생태환경건축학회)
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Development of Artificial Neural Network Model for Predicting the Optimal Setback Application of the Heating Systems

난방시스템 최적 셋백온도 적용시점 예측을 위한 인공신경망모델 개발

  • Baik, Yong Kyu (Department of Architecture, Seoil University) ;
  • Yoon, younju (Samsung C&T Corporation, Construction Technology Center) ;
  • Moon, Jin Woo (School of Architecture and Building Science, Chung-Ang University)
  • Received : 2016.04.26
  • Accepted : 2016.05.13
  • Published : 2016.06.30
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Abstract

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.

Keywords

References

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