As many studies prefer to focus on the accuracy of prediction models in model-based problems of chiller plants, the practicality and feasibility of the models used for field applications are often compromised for a superior performance. We herein present an online predictive control method based on linear regression model for a typical chiller plant from the perspective of real-time application to optimize its overall performance and energy consumption. A multi-input–single-output (MISO) controller is developed for predicting the global coefficient of performance (COP) of the chiller plant to reflect integral characteristics and interactions of components based on the multivariable linear regression (MLR) model. The input variables are much easier to obtain in a practical system compared to variables such as water flow and part load ratio, thereby rendering an excellent model capable of easy implementation and duplication. The performance of this approach is tested and evaluated in a real chiller plant system by comparing it to that obtained using a local control strategy. The results of this study indicate that the online predictive control strategy can enhance the global COP values by 5.35% on average and reduce electricity consumption by 2.70% daily compared to the local control strategy.

中文翻译：

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典型冷水机组系统的基于温度的多元线性回归模型在线预测控制方法

由于许多研究倾向于集中在基于模型的冷水机组问题中的预测模型的准确性，因此，通常会牺牲用于现场应用的模型的实用性和可行性来获得出色的性能。我们从实时应用的角度提出了一种基于线性回归模型的典型冷水机组在线预测控制方法，以优化其总体性能和能耗。开发了一种多输入单输出（MISO）控制器，用于基于多变量线性回归（MLR）模型预测冷水机组的整体性能系数（COP），以反映部件的整体特征和相互作用。相较于水流量和部分负载比等变量，在实际系统中更容易获得输入变量，从而提供了一个易于实现和复制的出色模型。通过将其与使用本地控制策略获得的性能进行比较，可以在实际的冷水机组系统中测试和评估此方法的性能。这项研究的结果表明，与局部控制策略相比，在线预测控制策略可以使全球COP值平均提高5.35％，每天可减少2.70％的用电量。