A Bayesian network model for the optimization of a chiller plant’s condenser water set point
Date Published |
12/2018
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Publication Type | Journal Article
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Authors | |
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DOI |
10.1080/19401493.2016.1269133
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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%). |
Journal |
Journal of Building Performance Simulation
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Volume |
11
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Year of Publication |
2018
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Issue |
1
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Pagination |
36 - 47
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ISSN Number |
1940-1493
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Short Title |
Journal of Building Performance Simulation
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Keywords | |
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Research Areas | |
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