%0 Journal Article
%K Modelica
%K Condenser water set point
%K Bayesian network
%K Regression-based optimization
%A Sen Huang
%A Ana Carolina Laurini Malara
%A Wangda Zuo
%A Michael D Sohn
%B Journal of Building Performance Simulation
%D 2018
%G eng
%N 1
%P 36 - 47
%R 10.1080/19401493.2016.1269133
%T A Bayesian network model for the optimization of a chiller plant’s condenser water set point
%V 11
%8 12/2018
%! Journal of Building Performance Simulation
%X <p>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%).</p>