臺灣博碩士論文加值系統

近年能源消耗急遽增加，若能源使用情況持續維持現況，至2050年全球能源需求將會提升50%，目前全球各國皆開始研究如何減少總能源的使用量，國際能源署指出全世界建築能耗約佔總能源使用的四成消耗，降低建築能源消耗是一個有效的節能辦法。
針對建築設計進行優選，應可有效降低建築能耗。但建築節能優選問題是ㄧ個高度複雜的非線性問題，因此本研究應用適合於求解複雜非線性問題的啟發式(heuristic)優選演算法─基因遺傳演算法(genetic algorithm, GA)與粒子群演算法(particle swarm optimization, PSO)，結合建築能耗模擬模式EnergyPlus，探討建築節能的最佳設計，並比較不同演算法之優選能力。
研究中優選模式的目標函數為最小化建築整體能耗，決策變數則包含開窗高度、窗頂位置、玻璃材質及外牆隔熱材，此外也設定了空調與日光調節等參數，利用GenOpt作為整合優選演算法與EnergyPlus的平台。
結果顯示，優選後所有牆面的窗頂位置都提高，南北兩面外牆的窗戶大小皆較原案例增大，而東西兩面牆的窗戶大小則是變小，其中以北面窗最大，西面窗最小，應該是避免西曬並考量維持室內足夠照明所致。在優選表現方面，不論是PSO或是GA的最佳解都有20%以上的節能成效。整體而言，GA具有較佳的尋優能力。

Base on the trend of energy consumption, the global energy demand will increase up to 50% in 2050. The whole world starts to find out the way to conserve energy, and the International Energy Agency pointed out that reducing building energy consumptions which account for 40% of global energy consumptions is an effective way for energy saving.
Although building design optimizations are highly complex nonlinear problems, it is a feasible way to decrease building energy consumptions. This study used GenOpt to integrate heuristic algorithms (genetic algorithm (GA) and particle swarm optimization (PSO)) and building energy consumption simulation program “EnergyPlus” to minimize the total building energy consumption. The heights and top locations of the windows, envelope insulation materials, and window materials are used as decision variables. The optimization performance of the two algorithms were also compared.
The results indicate that the optimized design selects higher top locations of windows, and to avoid the west sunshine, the sizes of the eastern and western facing windows are smaller than the original designs. On the contrary, the sizes of the southern and northern facing windows become larger to remain enough indoor illumination. The results of GA and PSO can both achieve energy savings more than 20%. In general, GA have the better optimization performance.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 前言 1
1.1研究背景 1
1.2 研究動機與目的 3
1.3研究流程與架構 3
第二章 文獻回顧 5
2.1建築模擬軟體 5
2.2建築模型 8
2.3目標函數與設計變數 9
2.4建築模擬優化案例 11
2.5文獻小結 13
第三章 研究方法 14
3.1優選模式 14
3.1.1開窗高度與窗頂位置 14
3.1.2玻璃材質 16
3.1.3外牆隔熱材質 17
3.1.4其他參定 17
3.2優選演算法 18
3.2.1遺傳演算法 18
3.2.2粒子群演算法 23
3.2.3 模式整合平台─GenOpt 26
3.3研究案例 27
第四章 結果與討論 33
4.1研究案例能耗分析 33
4.2 GA與PSO優選結果比較 34
4.3研究案例之優化成效 37
4.3.1 研究案例優選結果 37
4.3.2優化後之設計參數 39
4.3.3研究案例優化前後建築3D配置圖 39
第五章 結論與建議 42
5.1研究結論 42
5.2建議 42
參考文獻 43
中文文獻 43
英文文獻 44

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