Article

 

A Bayesian Network Model for the Optimization of a Chiller Plant’s Condenser Water Set Point Public Deposited

Downloadable Content

Download PDF
https://scholar.colorado.edu/concern/articles/05741s74b
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 behavior. To address this problem, we develop a Bayesian Network model and compare it to both a linear and a polynomial regression models 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 (RMSD) 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%).

Creator
Academic Affiliation
Journal Title
Journal Issue/Number
  • 1
Journal Volume
  • 11
Last Modified
  • 2020-06-30
Resource Type
Rights Statement
DOI
ISSN
  • 1940-1507
Language

Relationships

Items