A data driven performance assessment strategy for centralized chiller systems using data mining techniques and domain knowledge

Chillers are among the major energy consumers in building heating, ventilation and air conditioning systems and appropriate performance assessment of chiller systems is essential to ensuring their operational optimality while delivering satisfactory indoor thermal comfort. This paper presents a data driven performance assessment strategy for centralized chiller systems using multiple data mining and advanced visualization techniques. The energy consumption patterns of the chiller system were quantitatively and qualitatively analyzed by using the Conditional Inference Tree (CIT) and Agglomerative Hierarchical Clustering (AHC), and Association Rule Mining (ARM), respectively. A performance indicator of Coefficient of Performance (COP) Destruction (%) was introduced to represent the quality of the achieved COP. The performance of this strategy was evaluated using oneyear operating data of a centralized chiller system installed in a commercial building. The results showed that the data mining techniques can be effectively used for performance assessment of chiller systems. The results from the quantitative and qualitative analysis showed that the chiller performance was strongly influenced by the temperature difference across the evaporator. The system studied generally showed good performance when the part load ratio was above 45% and the chiller power ratio was above 50%, and it showed relatively poor performance when the temperature difference across the evaporator was below 3.1 degrees C.

Automated summary

The study utilized data mining techniques to assess the performance of chiller systems, finding that the performance is closely related to the temperature difference across the evaporator, as well as the part load ratio and chiller power ratio.