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A Bayesian Network Model for the Optimization of a Chiller Plant’s Condenser Water Set Point Public Deposited
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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%).
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- Academic Affiliation
- Journal Title
- Journal Issue/Number
- 1
- Journal Volume
- 11
- Last Modified
- 2020-06-30
- Resource Type
- Rights Statement
- DOI
- ISSN
- 1940-1507
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Thumbnail | Title | Date Uploaded | Visibility | Actions |
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J20_A_Bayesian_Network_Model_for_the_Optimization_of_a_Chiller_Plant_s_Condenser_Water_Set_Point.pdf | 2020-06-30 | Public | Download |