A Bayesian network model for the optimization of a chiller plant's condenser water set point. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Huang, Sen; Malara, Ana Carolina Laurini; Zuo, Wangda; Sohn, Michael D.
• Is Part Of: Journal of building performance simulation. Volume 11:Issue 1(2018)
• Page Start: 36
• Page End: 47
• Publication Date: 2018-01-02
• Subjects: Bayesian network -- condenser water set point -- regression-based optimization -- Modelica; 690.0113
• DOI: 10.1080/19401493.2016.1269133
• Languages: English
• ISSNs: 1940-1493

Abstract

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%).