This technique employs AI and big data analytics to precisely forecast cooling load demand and estimates the efficiency of different chiller combinations. Time-of-Use Pricing and optimal chiller load-interval are also considered for practical needs. Decision supports of optimal chiller configuration are provided to enhance energy conservation. Moreover, using real data feedback and model health examination, the model can do self-calibration and make evolution, continuously learn from the experts, and provide decision supports in good quality.1.Domain integrated model :
Well embedded domain knowledge and expert experiences in the model, making the suggestions keep up with the times and adapted to local conditions.
2.AI self-calibration:
The system gets feedback from the real data and makes self-improvement with error detection and model 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 interval and indoor-outdoor temperature converting formula making the model more precise and accurate.In high energy-consuming industries such as semiconductor and TFT-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, and panel fab can apply this technique for energy saving.