TY - JOUR KW - Modelica KW - Condenser water set point KW - Bayesian network KW - Regression-based optimization AU - Sen Huang AU - Ana Carolina Laurini Malara AU - Wangda Zuo AU - Michael D Sohn 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%).

BT - Journal of Building Performance Simulation DA - 12/2018 DO - 10.1080/19401493.2016.1269133 IS - 1 LA - eng N2 -

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%).

PY - 2018 SP - 36 EP - 47 ST - Journal of Building Performance Simulation T2 - Journal of Building Performance Simulation TI - A Bayesian network model for the optimization of a chiller plant’s condenser water set point VL - 11 SN - 1940-1493 ER -