KIEAE Journal

Korea Institute of Ecological Architecture and Environment (한국생태환경건축학회)
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Performance tests on the ANN model prediction accuracy for cooling load of buildings during the setback period

셋백기간 중 건물 냉방시스템 부하 예측을 위한 인공신경망모델 성능 평가

  • Park, Bo Rang (School of Architecture and Building Science, Chung-Ang University) ;
  • Choi, Eunji (School of Architecture and Building Science, Chung-Ang University) ;
  • Moon, Jin Woo (School of Architecture and Building Science, Chung-Ang University)
  • Received : 2017.07.09
  • Accepted : 2017.07.26
  • Published : 2017.08.30
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Abstract

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

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