| Technical Name |
AIBig Data Analytics for Energy SavingChiller Configuration Optimization |
| Project Operator |
NATIONAL TSING HUA UNIVERSITY |
| Project Host |
簡禎富 |
| Summary |
This technique employs AIbig data analytics to precisely forecast cooling load demandestimate efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load interval are also considered for practical needs. Decision supports of optimal chiller configuration are provided to enhance energy conservation.
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| Market Potential Analysis |
隨著半導體、電子產業的蓬勃發展，能源的消耗總量不斷上升，節約能源的議題備受關注。而台灣位處亞熱帶地區，夏季酷暑炎熱，為了使製程機台在適當的環境下運作，空調系統需要龐大的電力以維持廠房溫度。此技術將協助各高耗能產業解決過去冰水系統耗電量居高不下的問題。
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| Industrial Applicability |
In high energy consuming industries such as semiconductorTFT-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 fabpanel fab can apply this technique for energy saving.
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| Keyword |
Smart Energy Saving Artificial Intelligence Big Data Analytics Machine Learning Chiller System Chiller Optimization Chiller Combination Intelligent Manufacturing High-tech Industry High Energy Consuming Industry |
| Notes |
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