A proper selection of predictor variables would enhance the exploratory analysis of time series models while prompting practical strategies to optimize chiller system performance. This study explores essential operating variables to predict the time series of the coefficient of performance (COP) of a chiller system expressed as the cooling capacity output divided by the total electric power input of all components. Based on a huge set of historical operating data, hybrid ARIMA-regression models were developed by fitting 14 predictor variables other than the past COP-related terms. The most significant variable influencing predictability involves the part load ratio (PLR) and its order-3 lag terms lasting for 45 minutes. The system COP fluctuation is mainly governed by the PLR variation due to operating unnecessary chillers and non-pair up operation of system components. When chiller sequencing is properly implemented, the PLRs shift up with tempered variation. The paired component combinations lower the system electric power to maximize the system COP. The annual average system COP increases to 3.5538 from 3.3212 with a predicted electricity saving of 8.2955%. The novelty of this study is to highlight which variables and component operating statuses help improve the predictability of time series models while prioritizing practical strategies for performance improvement.

而促使实际战略，以优化冷却系统性能预测变量的正确选择将增强的时间序列模型的探索性分析。这项研究探索了必要的运行变量，以预测冷却系统的性能系数（COP）的时间序列，该时间序列表示为冷却能力输出除以所有组件的总电功率输入。基于大量的历史操作数据，通过拟合14个除过去与COP相关的术语以外的预测变量来开发混合ARIMA回归模型。影响可预测性的最重要变量涉及部分负荷比（PLR）及其3阶滞后项，持续45分钟。由于操作不必要的冷却器和系统组件的非配对操作，系统COP波动主要受PLR变化的影响。正确执行冷水机组排序后，PLR会随着温和变化而上升。配对的组件组合会降低系统电功率，以使系统COP最大化。年平均系统COP从3.3212增加到3.5538，预计节电8.29​​55％。这项研究的新颖性在于强调哪些变量和组件运行状态有助于提高时间序列模型的可预测性，同时优先考虑提高性能的实际策略。配对的组件组合会降低系统电功率，以使系统COP最大化。年平均系统COP从3.3212增加到3.5538，预计节电8.29​​55％。这项研究的新颖性在于强调哪些变量和组件运行状态有助于提高时间序列模型的可预测性，同时优先考虑提高性能的实用策略。配对的组件组合会降低系统电功率，以使系统COP最大化。年平均系统COP从3.3212增加到3.5538，预计节电8.29​​55％。这项研究的新颖性在于强调哪些变量和组件运行状态有助于提高时间序列模型的可预测性，同时优先考虑提高性能的实用策略。