@article{30286,
  keywords = {modelica, Condenser water set point, Bayesian network, regression-based optimization},
  author = {Sen Huang and Ana Carolina Laurini Malara and Wangda Zuo and Michael D Sohn},
  title = {A Bayesian network model for the optimization of a chiller plant’s condenser water set point},
  abstract = {<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>},
  year = {2018},
  journal = {Journal of Building Performance Simulation},
  volume = {11},
  pages = {36 - 47},
  month = {12/2018},
  issn = {1940-1493},
  doi = {10.1080/19401493.2016.1269133},
  language = {eng},
}