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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃國倉(Kuo-Tsang Huang) | |
| dc.contributor.author | Han-Hao Hsu | en |
| dc.contributor.author | 許涵皓 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:32:54Z | - |
| dc.date.available | 2018-08-17 | |
| dc.date.copyright | 2013-08-17 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-14 | |
| dc.identifier.citation | [1] 林憲德，2007，綠建築解說與評估手冊，內政部建築研究所。
[2] 陳志銘，2009，基因演算法結合EnergyPlus求解建築通風最佳配置，國立台灣科技大學碩士論文。 [3] 張斐章、陳莉，1995，目標函數對水庫即時操作之影響，臺灣水利，第39卷，第2期，pp.50-58。 [4] 周鵬程，2005，遺傳演算法原理與應用，全華圖書。 [5] 吳秉威，2005，以遺傳演算法求解PCB廠生產排程問題，台南科技大學碩士論文。 [6] 許志義，1994，多目標決策，五南圖書出版公司。 [7] 邱景升，2008，應用基因演算法於多目標土地使用規劃問題求解，逢甲大學碩士論文。 [8] K.F. Fong, V.I. Hanby , T.T. Chow, 2006, HVAC system optimization for energy management by evolutionary programming, Energy and Buildings 38, p220-231 [9] D. T. Dubrow, M. Krarti, 2010, Genetic-algorithm based approach to optimize building envelope design for residential buildings, Building and Environment 45 , p1574-1581 [10] L. Caldas, 2006, GENE_ARCH: an evolution-based generative design system for sustainable architecture, Intelligent Computing in Engineering and Architecture, Lecture Notes in Computer Science, vol. 4200, p. 109–118 [11] R. Alcal´A, 2003, Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms, Applied Intelligence 18, p155-177 [12] M. Sahu, B. Bhattacharjee, S.C. Kaushik, 2012, Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm, Energy and Buildings 53, p1-6 [13] W. Wang, R. Zmeureanu, H. Rivard, 2005, Applying multi-objective genetic algorithmsin green building design optimization, Building and Environment 40, p1512-1525 [14] W. Wang, H. Rivard, R. Zmeureanu, 2006, Floor shape optimization for green building design, Advanced Engineering Informatics 20, p363-378 [15] M. Mossolly, K.Ghali, N.Ghaddar, 2009, Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm, Energy 34, p58–66 [16] P. Wilde, Y. Rafiq, M. Beck, 2008, Uncertainties in predicting the impact of climate change on thermal performance of domestic buildings in the UK, BUILDING SERV ENG RES TECHNOL 29, p7-26 [17] X. Shi, 2011, Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm, Energy 36, p1659-1667 [18] V. ˇCongradac, F. Kuli´c, 2012, Recognition of the importance of using artificial neural networks and genetic algorithms to optimize chiller operation, Energy and Buildings 47, p651-658 [19] Engineering Reference, 2007, Version7.2, EnergyPlus Manual. [20] J. H. Holland, 1975, Adaptation in natural and artificial systems, Ann Arbor, MI: The University of Michigan Press. [21] D. E. Goldberg, 1989, Genetic algorithms in search, optimization, and machine learning, Reading, Mass: Addison-Wesley Pub. [22] M. Zeleny, 1982, Multiple Criteria Decision Making, New York: McGrew-Hill [23] J. L. Cohon, 1978, Multiobjective programming and planning, New York : Academic Press [24] R. L. Keeney, H. Raiffa, 1993, Decisions with multiple objectives : preferences and value tradeoffs, New York, NY, USA : Cambridge University Press | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60847 | - |
| dc.description.abstract | 目前市面上的既有建築之節能改善策略在選擇上非常多樣化，而相異之既有建築物因類型和設計的不同，往往也會有其最適用並且最有效率的改善策略組合。以往在策略的挑選上只能依靠建築師的工作經驗做為判斷的依據，卻缺乏一套有系統的挑選標準。所以本論文針對既有建築在改善策略的選擇問題上，透過遺傳演算法對眾多策略做適當的挑選，再結合建築能源模擬軟體EnergyPlus計算建築物的全年耗能，用以進行改善策略組合的最佳化運算分析，最後得到在固定的改善成本下之最佳改善策略搭配組合。
此外，在既有建築改善的過程中，除了考慮改建後的節能量之外，改善策略所需花費的成本往往也會影響最後選擇的決策。所以本論文將多目標規劃中的限制式法與權重法運用於挑選改善策略之系統上，對施工成本與節能效益之關係進行討論分析，並找出此兩個衝突的目標之間的權衡曲線(Trade-off line)，以提供決策者在挑選改善策略組合時之參考依據。 模擬結果顯示出，將遺傳演算法結合EnergyPlus應用於既有建築的改善策略組合挑選上，可以有效的降低最佳改善策略的挑選時間，而挑選到的改善策略組合雖然不保證為最佳組合，但會是非常接近最佳解的近似策略組合。最後由多目標規劃所得到的權衡曲線，可以有效預估使用成本與降低耗能量之間的關係，以提供決策者做策略選擇的判斷依據。 | zh_TW |
| dc.description.abstract | Nowadays, there are various choices of reformation strategies for saving energy for existing buildings. The different building types and designs also have the most appropriate and efficient combination of renovation strategies. However, in the past, the selection of strategies can only rely on architects’ experiences without any systemic principle. Thus, this study attempts to obtain the best combination of reformation strategies in fixed cost through the simulation model, EnergyPlus coupled with genetic algorithm. The former is utilized to analyze the annual energy consumption for improving the optimization of reformation strategies, and the latter is used to make a proper choice among alternatives.
In addition, the final decision will be affected by both the energy saving and the cost of reformation strategies during the building renovation process. Hence, this study also makes an effort to find a trade-off line for a collision between two objectives to provide a basis of selection, by means of the constraint method and weight method in multi-objective programming for selections of reformation strategies. The results represent that the use of Energyplus coupled with genetic algorithm for the application of selections of building renovation strategies can effectively shorten the searching time. Besides, albeit the selected combination of reformation strategies is not guaranteed to be the best, it will be close to the optimal solution at least. Finally, the trade-off line derived from multi-objective programming can effectively estimate the correlation between the use cost and the decrease of energy consumption to offer a basis of selection of strategies to decision-makers. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:32:54Z (GMT). No. of bitstreams: 1 ntu-102-R00622011-1.pdf: 1494776 bytes, checksum: 93758e9793f4c8863ec68be4646ce5ed (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 研究動機與目的 1 1-1 前言 1 1-2 研究動機與目的 2 1-3 研究流程 3 第二章 文獻回顧 5 第三章 研究方法 8 3-1 建築能源模擬軟體EnergyPlus 8 3-1.1 EnergyPlus簡介 8 3-1.2 EnergyPlus輸入資料 10 3-1.3 EnergyPlus特點 12 3-2 遺傳演算法 13 3-2.1 遺傳演算法基本理論 16 3-2.2 遺傳演算法步驟架構 18 3-2.3 遺傳演算法流程 24 3-3 多目標規劃 25 3-3.1 多目標規劃介紹 25 3-3.2 限制式法求解步驟 30 3-3.3 權重法求解步驟 32 第四章 研究對象與改善策略介紹 33 4-1 建築基線說明 33 4-1.1 基線建築基本資料 33 4-1.2 EnergyPlus建模資訊 36 4-1.3 基線建築模擬結果 39 4-2 改善策略介紹 41 4-2.1 改善策略方案介紹 41 4-2.2 改善策略成本 45 第五章 遺傳演算法與建築能源模擬軟體之偶合 48 5-1 編碼方式 48 5-2 初始親代設定 51 5-3 遺傳演算法情境設定與參數設定 51 5-4 於Matlab中修改執行EnergyPlus 57 5-5 偶合模式之運算流程 57 第六章 結果與討論 60 6-1 固定成本之改善策略選擇 60 6-2 應用多目標規劃於成本效益之分析 63 6-2.1 應用限制式法於權衡曲線之分析 63 6-2.2 應用權重法於權衡曲線之分析 72 6-3 應用權衡曲線於改善成本之回收年限探討 75 第七章 結論與建議 77 7-1 結論 77 7-2 建議 80 參考文獻 82 | |
| dc.language.iso | zh-TW | |
| dc.title | 綠建築改善策略之成本效益研究－應用遺傳算法於最佳化及多目標分析 | zh_TW |
| dc.title | Benefit and Cost Analysis of Green Building Renovation Strategies: An application of Genetic Algorithm in Optimization and Multi-objective Programming | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 胡明哲(Ming-Che Hu) | |
| dc.contributor.oralexamcommittee | 黃瑞隆,林子平 | |
| dc.subject.keyword | 既有建築改善,建築能源模擬,EnergyPlus,遺傳演算法,多目標規劃, | zh_TW |
| dc.subject.keyword | existing building renovation,building energy simulation,EnergyPlus,genetic algorithm,multi-objective programming, | en |
| dc.relation.page | 84 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-14 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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