產業脈動｜高耗能廠務系統節能潛力評估及人工智慧之應用機會

高科技晶圓廠房之廠務系統因具備高負載運行特性，其空調系統（Heating, Ventilation, and AirConditioning, HVAC）往往成為能效優化的核心熱點。為求精準評估節能潛力，本研究導入 SEMI S23 國際基準，建構等效電能轉換指標，將多元公用資源需求納入統一量測體系。透過開發「模組化能耗估測技術」，深度整合環境變動軌跡、即時設備負載與操作參數，建立高動態性的系統能耗推估模型。經由國內某 12 吋晶圓廠實證結果顯示，藉由本案之節能策略優化，年度可實現約 20 萬 kWh 之節電成效，具備顯著之減碳與經濟效益。文末進一步探討人工智慧（Artificial Intelligence, AI）於廠務端進行預測性節能管理之應用路徑，為未來綠色智慧廠房之建置提供實務參考。

High-tech semiconductor wafer fabs require facility systems to operate at continuous high loads, with the Heating, Ventilation, and Air Conditioning (HVAC，Heating, Ventilation, and Air Conditioning) system identified as a primary source of energy consumption. To precisely evaluate the energy-saving potential of these energy-intensive systems, this study incorporates the SEMI S23 standardized benchmark to establish an Equivalent Electric Energy Consumption metric, converting diverse utility requirements into a unified power consumption baseline. A modular energy estimation model was developed by consolidating environmental variables, equipment load profiles, and operational parameters to simulate and predict overall facility and sub-system energy trends. Using a domestic 12-inch wafer fab as a practical case study, the results confirm that the HVAC system accounts for a substantial proportion of total energy use. By implementing the proposed modular optimization strategies, an annual energy saving of approximately 200,000 kWh can be achieved, delivering significant carbon reduction and economic benefits. Furthermore, this article explores the potential of Artificial Intelligence (AI) for predictive energy management within facility systems, providing a technical roadmap for the future development of green smart factories.