自全球開始積極推動地球的永續發展，永續建築成為當今建築設計的主流。以保護地球為出發點的建築設計，並同時打造人類健康的生活環境空間。建築物能夠透過達到綠建築指標來實現永續的建築設計，其中室內環境指標與能源耗用量是綠建築指標當中兩大的評估標準。這兩項目標會存在著互相衝突的關係，例如當建築的窗口設計越大能夠提高室內採光量，然而卻會因為過多的陽光使得熱舒適度降低，而需要使用冷卻設備來降低溫度，進而使得能耗增加。因此建築設計需要透過權衡目標值才能設計出顧慮到所有性能指標的建築設計。
本研究以提升建築室內的採光量與降低能源使用量作為改變建築窗口設計的兩大目標，使用Rhino/Grasshopper對建築模型建立參數化的窗口設計；使用Grasshopper外掛程式Ladybug 與Honeybee做為採光與能耗模擬分析的工具。本研究基於NSGA-II(Nondominated Sorting Genetic Algorithm II)開發採光與能耗雙重目標最佳化模式(DEBOO)，透過參數化建模即時變換建築設計以及採光、能源模擬引擎模擬分析出空間自主採光量(sDA)與能源耗用量(EUI)；針對建築的性能表現，對建築窗口設計進行最佳化演算產生柏拉圖最適解。DEBOO與MOPOS以及多目標演算軟體Octopus進行驗證比較。比較結果證明DEBOO能在所有可行的設計方案中尋找採光與能耗表現最佳的建築窗口設計，同時能獲取到最多的設計方案，提供設計者依照建築性能選擇符合永續指標的建築設計。

As people around the world have growing concerns about sustainability development, currently sustainable building design attracts a great amount of attention. Sustainable building design dedicates to protect the environment and enhance human well-being. The indoor environment and energy-consuming are two major criteria among all categories of evaluation from the green building rating system and they usually are against each other. For instance, when bigger windows can increase daylight autonomy, too much daylight will heat up the indoor temperature. In such case, residents will need to use the cooling equipment to maintain a comfortable indoor environment causing increase in the energy demand. Therefore, when designers performs the design tasks, they have to understand the tradeoff relationship between daylight autonomy and energy consumption to seek a balanced performance.
This study proposes an optimization model to enhance daylight autonomy and reduce energy consumption. The model is called DEBOO, which is based on NSGA-II(Nondominated Sorting Genetic Algorithm II) to find the best windows design for the building. DEBOO includes parametric design, building performance simulation, and bi-objective optimization. Using Rhino/Grasshopper to build the parametric model to change design quickly, and use daylight and energy simulation engine to simulate the spatial daylight autonomy and energy use intensity. It then performs optimization based on the simulation results. DEBOO helps designers explore numerous window designs and generate the Pareto Front that consists of a set of non-dominated designs in terms of two objectives: to maximize daylight autonomy and to minimize energy consumption. . DEBOO is compared with another popular multi-objective metaheuristic: MOPSO, and the commercial software package Octopus. The result verifies that DEBOO can find windows optimal designs with the most efficient performance. DEBOO also has more non-dominated design options than the other two tools. Thus, DEBOO can provides designers with more chooses to determine the most balanced design.

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