基於全球對暖化及能源永續發展的關注，製造系統的能源管理是邁向工業4.0的一個關鍵議題，因此需要建立數據分析機制來促進高科技廠房對能源優化的需求。本文提出一個數據分析框架，使優化模型能夠被執行在工業空調系統上，該模型包括中央的外氣空調箱(MAU)和冷卻乾盤管系統(DCC)。所提出的優化模型使用迴歸模型來學習能源因子的相關性，並使用遺傳算法來優化能源使用。並提出協作區域控制，以實現更有效的DCC控制。經過實地驗證，所提出的數據分析框架之優化模型執行優於原本的比例積分微分(PID)控制器。DCC和MAU優化模型分別降低36.3%和24.16%的電力成本。這項研究有助於建立高科技廠房之空調系統中的數據分析框架。

Industrial energy management is one of the key issues in Industrial 4.0 era owing to global warming and sustainability. It reveals the need of a data analytics framework to facilitate fab energy optimization. This paper proposes a data analytics framework to enable the execution of optimization models in the fab air conditioning system, consisting of a centralized make-up air unit (MAU) and a set of dry-cooling coils (DCCs). The proposed optimization model uses regression models for learning correlated energy factors, and genetic algorithms for optimizing of energy usage. Collaborative regional-control in the data proposed framework is proposed to achieve efficient DCCs control. The proposed data analytics framework empowered by optimization is shown in real case to outperform a proportional-integral-derivative controller. The DCC and MAU optimization model performed 36.3% and 24.16% reduction of electricity cost, respectively. This study contributed to formalize data analytics framework in industrial air conditioning systems.

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