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%).

10aBayesian network10aCondenser water set point10amodelica10aregression-based optimization1 aHuang, Sen1 aMalara, Ana, Carolina L1 aZuo, Wangda1 aSohn, Michael, D. uhttps://energyanalysis.lbl.gov/publications/bayesian-network-model-optimization02739nas a2200229 4500008004100000022001300041245010400054210006900158260001200227300001200239490000800251520198000259653003002239653002902269653001302298653002702311653003202338100001502370700001602385700002202401856008602423 2017 eng d a0360132300aImproved cooling tower control of legacy chiller plants by optimizing the condenser water set point0 aImproved cooling tower control of legacy chiller plants by optim c01/2017 a33 - 460 v1113 aAchieving 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.