工業4.0的概念導入，將人類所知的科技帶向一個新的層次，隨著企業的營業目標逐漸由過去強調獲利成長，逐漸移轉成全面性永續經營的策略，也使能源消耗的危機更受重視，智慧能源管理已成為產業轉型中不可或缺的角色。在智慧工廠中，除了提升生產設備之外，企業更藉由改善傳統電能管理模式，對設備進行大數據的收集與分析；總括來說，智能管理是有效調度、監控、整合設備執行預測式維護管理，並依數據分析開發出客製化之能源管理平台。本研究以半導體製造業廠務系統為例，透過個案探討在工業4.0帶動智慧能源管理的趨勢中，應用失效模式與效應分析、TRIZ方法歸納符合實務需求之技術，發展可改善潛在失效模式的智慧偵測元件，由物聯網架構應用與大數據蒐集、傳輸等過程，針對系統做故障診斷技術評估進行預測式保養，提升智能管理應用領域中的價值所在，並作為相關產業邁向智能化的實務參考並提出管理建議。

The advent of Industry 4.0 takes our understanding of technology to a whole new level. The pursuit of profitability is gradually being replaced by business strategies that focus on comprehensive and sustainable operations. As a consequence, the looming energy crisis has become the center of attention, making smart energy management solutions an indispensable cornerstone of industry transformation. For intelligent factories, in addition to upgrading manufacturing equipment, businesses can improve upon traditional models of energy management by collecting and analyzing big data generated by the equipment. Smart energy management, in sum, is a system that effectively coordinates, monitors, integrates, manages, and predicts the operation of multiple sets of equipment, creating a customized energy management platform for every business based on data analytics. The present study is a case study on the facility management system adopted by semiconductor manufacturers. The author discusses the developmental trends in smart energy management within the context of Industry 4.0 based on “failure modes and effects analysis (FMEA)” and the “theory of inventive problem solving (TRIZ).” Building on the results, the author summarizes the potential technologies that meet practical needs and the development of intelligent electrical components that address potential failure modes. Finally, through the application of Internet of Things (IoT) and big data collection and transmission, businesses can conduct predictive maintenance on their in-service equipment to prevent system downtime, realizing the true benefits of intelligent management. The author hopes that the findings of this study can offer useful insights for relevant industries seeking to transform their businesses intelligently.

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