This study explores the energy-saving efficiency of a complex water-cooled chiller system composed of multiple chillers, cooling towerswater pumps connected by circulation pipelines. The deep learning modeling method driven by big data provides optimized parameters of the water-cooled chiller system to reduce the power consumption of the chiller system. Experiments show that this technology can increase the power saving efficiency of AU Optronics Longtan L5B factory by 2.42. This method has been implemented in many factories of AUO.

This technique employs AIbig data analytics to precisely forecast cooling load demandestimates the efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load-interval are also considered for practical needs. Decision supports of optimal chiller configuration is provided to enhance energy conservation. Moreover, using real data feedbackmodel health examination, the model can do self-calibrationmake evolution, continuously learn from the experts,provide decision supports in good quality.