Very few studies have considered modelling the statuses of loading and component combinations (LC) in chiller systems. This study uses a decision tree (DT) and a neural network (NN) to model accurately six output levels of LC in terms of 11 predictor variables. The NN model gave a higher correct prediction of 89.52% than 84.44% in the DT model. A variable dropout analysis from the DT and NN models generalizes the top five significant variables: the statuses of cooling towers and primary chilled water pumps, and the supply temperature, return temperature and flow rate of chilled water. The system coefficient of performance (SCOP) modelled by NN has an improved R² of 84.18% versus 74.62% when the significant variables are included as inputs. The SCOP maximization brings three optimal strategies—implementing chiller sequencing, operating components in pairs and optimizing chilled water supply temperature—which reduce CO₂ emissions by 99,815–346,987 kg.

很少有研究考虑对冷水机组系统中的负载和组件组合 (LC) 状态进行建模。本研究使用决策树 (DT) 和神经网络 (NN) 根据 11 个预测变量对 LC 的六个输出级别进行准确建模。NN 模型的正确预测率为 89.52%，高于 DT 模型的 84.44%。来自 DT 和 NN 模型的变量 dropout 分析概括了前五个重要变量：冷却塔和初级冷冻水泵的状态，以及冷冻水的供水温度、返回温度和流量。由 NN 建模的系统性能系数 (SCOP) 具有改进的R ²84.18% 与 74.62% 的显着变量作为输入。SCOP 最大化带来了三种优化策略——实施冷水机组排序、成对运行组件和优化冷冻水供应温度——这将 CO ₂排放量减少了 99,815–346,987 kg。