Chiller system is one of the most electricity-consuming parts of factory facilities. Thus, optimize chiller configuration as well as minimize chiller power consumption become a crucial issue. Conventionally, operations of chillers seriously rely on engineers’ practical experiences. However, various uncertainties, including changeable weather and complicated chiller combinations, lead to inconsistent decisions of switching chiller machines. This technique employs AI and big data analytics to provide decision supports of chiller configuration optimization for industries and reduce variability of decisions as well as energy waste.