TY - JOUR
T1 - A Bayesian network model for the optimization of a chiller plant’s condenser water set point
AU - Huang, Sen
AU - Malara, Ana Carolina Laurini
AU - Zuo, Wangda
AU - Sohn, Michael D.
N1 - Funding Information:
This research was supported by the U.S. Department of Defense under the ESTCP program and the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy [under grant number DE-AC02-05CH11231]. The second author performed this research as a visiting student at the University of Miami with the support of Brazilian Scientific Mobility Program.
Funding Information:
This research was supported by the U.S. Department of Defense under the ESTCP program and the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy [under grant number DE-AC02-05CH11231]. The second author performed this research as a visiting student at the University of Miami with the support of Brazilian Scientific Mobility Program. The authors thank Marco Bonvini, Michael Wetter, Mary Ann Piette, Jessica Granderson, Oren Schetrit, Rong Lily Hu and Guanjing Lin for the support provided through the research. This research also emerged from the Annex 60 project, an international project conducted under the umbrella of the International Energy Agency (IEA) within the Energy in Buildings and Communities (EBC) Programme. Annex 60 will develop and demonstrate new-generation computational tools for building and community energy systems based on Modelica, Functional Mockup Interface and BIM standards.
Funding Information:
© This material is published by permission of the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies for the US Department of Energy under Contract No. DE-AC02-05CH11231. The US Government retains for itself, and others acting on its behalf, a paid-up, non-exclusive, and irrevocable worldwide licence in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Publisher Copyright:
© This material is published by permission of the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies for the US Department of Energy under Contract No. DE-AC02-05CH11231.
PY - 2018/1/2
Y1 - 2018/1/2
N2 - 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%).
AB - 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%).
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U2 - 10.1080/19401493.2016.1269133
DO - 10.1080/19401493.2016.1269133
M3 - Article
AN - SCOPUS:85007275392
SN - 1940-1493
VL - 11
SP - 36
EP - 47
JO - Journal of Building Performance Simulation
JF - Journal of Building Performance Simulation
IS - 1
ER -
