TY - JOUR
T1 - A Bayesian network model for the optimization of a chiller plantâ€™s condenser water set point
JF - Journal of Building Performance Simulation
Y1 - 2018/12//
SP - 36
EP - 47
A1 - Sen Huang
A1 - Ana Carolina Laurini Malara
A1 - Wangda Zuo
A1 - Michael D. Sohn
KW - Bayesian network
KW - Condenser water set point
KW - modelica
KW - regression-based optimization
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%).
VL - 11
IS - 1
JO - Journal of Building Performance Simulation
ER -
TY - JOUR
T1 - Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point
JF - Building and Environment
Y1 - 2017/01//
SP - 33
EP - 46
A1 - Sen Huang
A1 - Wangda Zuo
A1 - Michael D. Sohn
KW - Condenser water set point
KW - Model predictive control
KW - modelica
KW - Optimization frequency
KW - Optimization starting point
AB - Achieving the optimal control of cooling towers is critical to the energy-efficient operation of current or legacy chiller plants. Although many promising control methods have been proposed, limitations in their applications exist for legacy chiller plants. For example, some methods require the change of the plant's overall control structure, which can be difficult to legacy chiller plants; some methods are too complicated and computationally intensive to implement in old building control systems. To address the above issues, we develop an operational support system. This system employs a model predictive control scheme to optimize the condenser water set point and can be applied in chiller plants without changes in the control structure. To further facilitate the implementation, we propose to increase the optimization accuracy by selecting a better starting point. The results from a case study with a real legacy chiller plant in Washington D.C. show that the proposed operational support system can achieve up to around 9.67% annual energy consumption savings for chillers and cooling towers. The results also show the proposed starting point selection method can achieve a better accuracy and a faster computational speed than commonly used methods. In addition, we find that we can select a lower optimization frequency for the studied case since the impact of the optimization frequency on the energy savings is not significant while a lower optimization frequency does reduce the computational demand to a great extent.
VL - 111
JO - Building and Environment
ER -