건물 냉방시스템의 예측제어를 위한 인공신경망 모델 개발

• Published in: KIEAE Journal Vol. 17; no. 5; pp. 69 - 76
• Main Authors: 강인성(Kang, In-Sung), 양영권(Yang, Young-Kwon), 이효은(Lee, Hyo-Eun), 박진철(Park, Jin-Chul), 문진우(Moon, Jin-Woo)
• Format: Journal Article
• Language: Korean
• Published: 한국생태환경건축학회 2017
• Subjects: 건축공학; Predictive Control; 최적제어변수; 예측제어; 인공신경망; Optimal Control Variables; Artificial Neural Network; VRF 냉방시스템; VRF Cooling System; VRF
• ISSN: 2288-968X; 2288-9698
• DOI: 10.12813/kieae.2017.17.5.069

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.