This technique employs AI and big data analytics to precisely forecast cooling load demand and estimate 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.

此技術可在多個不確定因素下(如：變動的天氣與複雜的冰機組合)，透過AI大數據分析提供精準的冷凍噸預測，並模擬各種開關組合的效益，同時考量時間電價、冰機組合最適負載等實務上需求，進而提供冰水系統調度優化之節能決策支援。

In high energy consuming industries such as semiconductor and TFT-LCD manufacturing, chiller system is indispensable to factories but require huge energy consumption. This technique can conduct pre-assessment without additional facilities investments. Including wafer fab, backend fab and panel fab can apply this technique for energy saving.