TY - JOUR
T1 - Application of artificial neural networks for determining energy-efficient operating set-points of the VRF cooling system
AU - Chung, Min Hee
AU - Yang, Young Kwon
AU - Lee, Kwang Ho
AU - Lee, Je Hyeon
AU - Moon, Jin Woo
N1 - Funding Information:
This research was supported by the Department of Digital Appliance R&D Team, Samsung Electronics and by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT and Future Planning (grant number 2015R1A1A1A05001142 ).
Publisher Copyright:
© 2017
PY - 2017/11/15
Y1 - 2017/11/15
N2 - The aim of this study was to develop an Artificial Neural Network (ANN) model that can predict the amount of cooling energy consumption for the different settings of the variable refrigerant flow (VRF) cooling system's control variables. Matrix laboratory (MATLAB) and its neural network toolbox were used for the ANN model development and test performance. For the model training and performance evaluation, data sets were collected through the field measurement. Four steps were conducted in the development process: initial model development, input variable selection, model optimization, and performance evaluation. In the initial model development and input variable selection process, seven input variables were selected as input neurons: TEMPOUT, HUMIDOUT, TEMPIN, LOADCOOL, TEMPSA, TEMPCOND, and PRESCOND. In addition, the initial model was optimized to have 2 hidden layers, 15 hidden neurons in each hidden layer, a learning rate of 0.3, and a momentum of 0.3. The optimized model demonstrated its prediction accuracy within the recommended level, thus proved its potential for application in the control algorithm for creating a comfortable indoor thermal environment in an energy-efficient manner.
AB - The aim of this study was to develop an Artificial Neural Network (ANN) model that can predict the amount of cooling energy consumption for the different settings of the variable refrigerant flow (VRF) cooling system's control variables. Matrix laboratory (MATLAB) and its neural network toolbox were used for the ANN model development and test performance. For the model training and performance evaluation, data sets were collected through the field measurement. Four steps were conducted in the development process: initial model development, input variable selection, model optimization, and performance evaluation. In the initial model development and input variable selection process, seven input variables were selected as input neurons: TEMPOUT, HUMIDOUT, TEMPIN, LOADCOOL, TEMPSA, TEMPCOND, and PRESCOND. In addition, the initial model was optimized to have 2 hidden layers, 15 hidden neurons in each hidden layer, a learning rate of 0.3, and a momentum of 0.3. The optimized model demonstrated its prediction accuracy within the recommended level, thus proved its potential for application in the control algorithm for creating a comfortable indoor thermal environment in an energy-efficient manner.
KW - Artificial neural network
KW - Condenser fluid pressure set-point
KW - Condenser fluid temperature set-point
KW - Predictive controls
KW - Refrigeration evaporation temperature set-point
KW - Supply air temperature set-point
UR - http://www.scopus.com/inward/record.url?scp=85028565466&partnerID=8YFLogxK
U2 - 10.1016/j.buildenv.2017.08.044
DO - 10.1016/j.buildenv.2017.08.044
M3 - Article
AN - SCOPUS:85028565466
SN - 0360-1323
VL - 125
SP - 77
EP - 87
JO - Building and Environment
JF - Building and Environment
ER -
