A Bayesian network model for the optimization of a chiller plant’s condenser water set point

Sen Huang, Ana Carolina Laurini Malara, Wangda Zuo, Michael D. Sohn

Research output: Contribution to journalArticle

3 Scopus citations


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%).

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalJournal of Building Performance Simulation
StateAccepted/In press - Dec 21 2016



  • Bayesian network
  • condenser water set point
  • Modelica
  • regression-based optimization

ASJC Scopus subject areas

  • Architecture
  • Modeling and Simulation
  • Building and Construction
  • Computer Science Applications

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