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.

"1. Domain integrated model: Well embedded domain knowledgeexpert experiences in the model,
making the suggestions keep up with the timesadapted to local conditions.
2. AI self-calibration: The system gets feedback from the real datamakes self-improvement
with error detectionmodel evolution. Keeping the model healthy can make sure the suggestion of the decision is in good quality.
3. Precise modeling: Exclusive research for best chiller efficient performance intervalindoor-outdoor temperature converting formula making the model more preciseaccurate."

In high energy-consuming industries such as semiconductorTFT-LCD manufacturing high-tech industries, the chiller water system is indispensable to factories but requires huge energy consumption. This AI decision support technique can conduct pre-assessment without additional facilities investments. Including wafer fab, backend fab,panel fab can apply this technique for energy saving.

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