由於近年來工商產業的發展迅速,致使許多的大型商辦建築如雨後春筍般林立,據統計一般商辦大樓內的空調能耗即佔百分之五十的整體建築能耗,因此建築內部的整體空調系統即為本研究之重點。在實際運轉工況下,空調系統會受到運轉時數增加、維修不良等因素,造成系統設備老舊退化及故障頻率增加。有鑑於此,如何開發出一套自動即時監控系統,達到故障預警協助管理保養與即時故障排除,避免系統運轉損耗,維持高效率運轉周期,儼然成為一門重要的學問。 本研究係以EnergyPlus建構一棟實際辦公大樓,輸入實際內部負載(如:照明耗電、人員密度、設備熱得等)及空調系統的設備規格,進而模擬全年逐時之理想運轉工況及能耗作為基準資料,對照模擬情境之即時運轉工況,並參照各種不同技術文獻所歸納整理出空調系統中常見的故障情形,套入性能迴歸參考模式中並以Q統計手法分隔出系統故障原因,藉此以提供業主及現場維護人員即時且精確的資訊。 研究結果證明以EnergyPlus模擬工況作為運轉基準資料,配合所應用之故障分析方法可有效地診斷出系統異常運轉點,進而成為一全新節源技術手段之開端,以俾後續之相關研究與開發。
Substantial growth in the industry and commerce in the recent years has caused more and more big commercial buildings to be constructed. According to the statistics, a standard commercial building's air conditioning accounts for 50% of the whole building's energy. Therefore, this research focuses on the operation diagnosis of building's air conditioning system. In the actual operation, the air conditioning system is affected by factors such as running hours and poor maintenance. As a result, the equipment depreciates quickly and increases its malfunction. It is therefore very important to establish an automatic monitoring system that can detect any malfunction once it occurs. And also help to manage the maintenance and immediately eliminate the malfunction to avoid any energy lost. This will make sure the operation is at its effective optimal. This research uses EnergyPlus to model a real office case, all the actual data and the schedule of air conditioning system were inputted (e.g. light energy, population density, equipment heat gain etc.). After, model was calibrated with the actual operation condition and energy consumption, this research applied multiple regression and Q-statistic to diagnosis the system and further distinguish the malfunction reasons in the real-time operation. The results show that real-time self-fault-diagnosis with EnergyPlus for the air-conditioning system can work efficiently, thus become a state-of-the-art energy-saving method, and be worthy for the relevant research and development in the future.