應用人工免疫演算法於辦公廳類綠建築之空調設備成本最佳化設計

Abstract

空調之耗電量佔建物總耗電量非常大的比例，且與其相關之綠成本也相當地高，但如有良好之空調設備配置，再搭配適當之空調節能技術，可達到最佳之節能狀態。因此為使在追求空調節能之下，同時又可兼顧成本考量，本研究利用人工免疫演算法之優選能力，求解在符合臺灣綠建築規範EEWH日常節能指標之空調節能指標下，成本最低之空調設備配置。成本部分若只單純考慮設備費用，並無法反應整體系統運轉之能耗情況，因此納入了耗電量所造成的費用部分考量，以後續15年之運轉耗電量費用來反映系統之能耗狀況。 本研究透過窮舉法驗證人工免疫演算法之性能，結果顯示，人工免疫演算法所求得之近似最佳解非常逼近最佳解，誤差僅1.06％，運算時間卻相差約三萬倍。由此驗證可得知，以人工免疫演算法求解最佳空調設備配置不僅效果佳且效率高。另外也利用動態能耗分析軟體eQUEST來驗證耗電量計算之部分，結果顯示本系統計算之耗電量結果與透過模擬得到之耗電量差距皆在10%內，為可接受之範圍。 透過本系統，使用者可依需求選擇空調系統和期望之空調系統節能效率（EAC值），從多樣的空調設備中快速地得到符合綠建築規範且成本最佳之空調設備配置，以作為設計過程中當作參考之用。

The electric power consumption of the air-conditioning system occupies a large proportion of the total electric power consumption in buildings, and the cost related to air-conditioning is also a large sum. However, a good selection of air-conditioning system with appropriate energy-saving technologies can help achieve maximum energy saving. In order to consider the energy-saving effect and the cost of air-conditioning system at the same time, this research aims to develop a smart system for air-conditioning design optimization for commercial green buildings. The artificial immune algorithm (AIA) is used in the design optimization system, which takes account of the Taiwanese green building standards, EEWH, and the minimization of energy consumption and costs. For the cost of the air-conditioning system, both the equipment cost and the energy consumption costs in the first 15 years of operation are considered. To validate the efficiency and effectiveness of the artificial immune algorithm (AIA), the results of AIA were compared to the enumerated results. The comparison results showed that the result difference between AIA and enumeration was merely 1.06%, while enumeration took more than 30,000 times the computation time of AIA. Besides, the calculated energy consumption result from the proposed system was compared to that of eQUEST, and the result difference fell well within 10% and was deemed acceptable. Through this optimization system, the user can choose the air-conditioning system and the expected EAC value they want, and then get the best combination of the equipment which meet the green building guidelines and spend the optimal cost. This solution can be taken as reference at the design phase.