

Title | A Bayesian network model for the optimization of a chiller plant’s condenser water set point |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Sen Huang, Ana Carolina L Malara, Wangda Zuo, Michael D Sohn |
Journal | Journal of Building Performance Simulation |
Volume | 11 |
Issue | 1 |
Pagination | 36 - 47 |
Date Published | 12/2018 |
ISSN | 1940-1493 |
Keywords | Bayesian network, Condenser water set point, modelica, regression-based optimization |
Abstract | 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%). |
DOI | 10.1080/19401493.2016.1269133 |
Short Title | Journal of Building Performance Simulation |