Purpose

This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.

Design/methodology/approach

First, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.

Findings

The strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.

Research limitations/implications

This study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.

Practical implications

The strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.

Originality/value

Previous studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

中文翻译：

---

带有市场篮子分析的冷水机系统性能管理

目的

本研究旨在应用关联规则挖掘 (ARM) 来揭示冷水机组系统运行组件之间的特定关联，并提高其性能系数 (COP)，从而减少中央空调建筑物的用电量。

设计/方法/方法

首先，确定了 13 个操作变量，包括系统组件的温度和流量及其开关状态的测量值。在执行 ARM 之前，变量被分为四个 bin。制定了强有力的规则来将变量和切换状态与不同的 COP 类别相关联。

发现

强规则解释了实践冷却器排序和优化变量的优先级的现有限制。基于最高 COP 级别的严格规则，最佳运行策略包括在高负载运行期间成对重新安排冷却器及其相关组件。重置冷冻水供应温度是下一个最佳策略，其次是重置冷凝器进水温度，但受操作限制。

研究限制/影响

本研究仅考虑具有四个 bin 的偶数频率方法。可以使用其他离散化方法和不同数量的类来完成复制工作，以比较优化变量的 bin 范围的潜在差异。

实际影响

ARM 确定的强规则突出了变量与高或低 COP 之间的关联。这支持选择关键变量和系统组件的运行状态以最大化 COP。可以进一步评估量身定制的优化策略和相关的节电。

原创性/价值

以前的研究将 ARM 应用于冷水机故障检测，但没有考虑不同组件相互作用下的系统性能。这项研究的新颖之处在于它展示了 ARM 在发现过去操作数据中的关联方面的智能。这使得识别量身定​​制的能源管理机会成为可能，这对于所有工程系统都是必不可少的。ARM 没有典型回归和黑盒模型的预测误差。