TY - JOUR
T1 - A Bayesian network model for the optimization of a chiller plantâ€™s condenser water set point
JF - Journal of Building Performance Simulation
Y1 - 2018/12//
SP - 36
EP - 47
A1 - Sen Huang
A1 - Ana Carolina Laurini Malara
A1 - Wangda Zuo
A1 - Michael D. Sohn
KW - Bayesian network
KW - Condenser water set point
KW - modelica
KW - regression-based optimization
AB - To implement the condenser water set point optimization, one can employ a regression model. However, existing regression-based methods have difficulties to handle non-linear chiller plant behaviour. To address this problem, we develop a Bayesian network model and compare it to both a linear and a polynomial regression model via a case study. The results show that the Bayesian network model can predict the optimal condenser water set points with a lower root mean square deviation for both a mild month and a summer month than the linear and the polynomial models. The energy-saving ratios by the Bayesian network model are 25.92% and 1.39% for the mild month and the summer month, respectively. As a comparison, the energy-saving ratios by the linear and the polynomial models are less than 19.00% for the mild month and even lead to more energy consumption in the summer month (up to 3.73%).
VL - 11
IS - 1
JO - Journal of Building Performance Simulation
DO - 10.1080/19401493.2016.1269133
ER -