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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃國倉 | |
dc.contributor.author | Shih-Chen Chen | en |
dc.contributor.author | 陳世禎 | zh_TW |
dc.date.accessioned | 2021-06-17T08:24:16Z | - |
dc.date.available | 2029-09-30 | |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-13 | |
dc.identifier.citation | 參考資料
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74205 | - |
dc.description.abstract | 辦公建築之生命週期中,有超過80%的耗能集中於日常使用之階段,為建築生命週期中碳排放佔比最大的階段。本研究期望建立一建築碳排評價之系統,期望建立一計算流程來評估其日常使用階段之碳排量。本研究主要分成兩個部分:(1)辦公建築之全年耗能推估、(2)建築碳排評價系統之建置。
在辦公建築全年耗能推估中,本研究收集台灣建築之組成因子,並利用這些因子結合建立之耗能計算流程,來推估建築全年耗能量,耗能項目包含空調、換氣設備、照明、電梯設備、辦公事務機器等六項,將統合這些項目個別之耗能量總和,以作為評判建築耗能表現之指標。另外,為了使用者方便評估使用,在空調耗能之推估上,利用類神經網路預測模型,結合空調耗能與建築組成因子,提供使用者預測其全年建築空調耗能。 建築碳排評價系統之建置上,利用建立建築基線模型之概念,來評估建築碳排之表現。本研究利用建築組成因子與台灣之法規,建立欲評估建築之對照基線案例,並根據設計案例與基線案例之比值(設計與基線碳排比)來做為碳排評比之依據,建立一評分查詢之系統。 研究結果顯示,台灣之辦公建築耗能大約以照明、空調、事務機器之碳排各佔30%,其他碳排約佔10-13%,建築耗電強度(EUI)則根據辦公建築使用時程不同以及設計情況不同,大約落在50(kWh/m2)至180(kWh/m2)之間。碳排評價系統之建置上,則以Gamma分布擬合建築設計與基線碳排比之分布,建立評分查詢表。使用者可透過耗能計算之流程取得建築各項耗能之資訊,以及搭配類神經網路預測建築之空調耗能,來計算建築之全年空調耗能,並由經濟部所公布的電力排放係數,取得耗能予碳排之間的關係。並藉由與欲評估模型對比之基線模型之建立,根據碳排評分表來了解建築耗能之表現。 | zh_TW |
dc.description.abstract | More than 80% of energy consumption in building life cycle is during daily usage phase, which is the main phase of carbon emissions of building life cycle. In this study, a building carbon emission benchmarking system was built to evaluate the carbon emissions during daily usage phase of office building. In this study, we try to achieve two main goal, the first is to estimate the energy consumption in a year by the design factors of office building, the second is to build a carbon emission benchmarking system to let the users easily know their building energy performance.
To estimate the energy consumption of office building, we try to collect the design factors of office building in Taiwan and combine with the calculation process in this study to estimate the building energy consumption of a whole year. The energy consumption in a typical office building includes: HVAC system, ventilation system, lighting, water pump, elevator equipment, and office equipment, these energy consumption items would be calculated and sum up to identify the energy performance. On the other hand, to provide a convenient benchmarking system, Neuron network prediction model was introduced in this study to let the users to get the HVAC system energy consumption in a whole year because of the complexity of estimating HVAC energy consumption compare to other energy demands. To build up a benchmarking system, a baseline model was introduced in this study to evaluate the carbon emission performance of office building. Combining the building design factors and building regulations in Taiwan, we can build up a baseline building for every proposed building. And by the Ministry of Economic Affairs in Taiwan, we can get the coefficient of carbon emission to transfer the energy consumption to the amount of carbon emission. The ratio of the proposed building`s carbon emission and baseline building`s carbon emission is called Design and Baseline Carbon Emission Ratio (DBCER), and DBCER is used to evaluate the performance of the office building. The results indicate that the lighting, office equipment and HVAC system each shares about 30% of total carbon emission in a whole year, and the other energy consumption items share about 10%-13% of the total carbon emission. The Energy Usage Intensity(EUI) value is between 50(kWh/m2) to 180(kWh/m2) generally in Taiwan. In the benchmarking system, Gamma distribution was used to fit the DBCER value, and the fitted curve was used to make a lookup table for the users to estimate their building energy performance by calculating the DBCER value and get the building score of the proposed building. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:24:16Z (GMT). No. of bitstreams: 1 ntu-108-R06622010-1.pdf: 3579186 bytes, checksum: 65d9dcd60a5861d6a39f34e32419d894 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 目錄
謝誌 0 摘要 3 ABSTRACT 4 目錄 6 圖目錄 9 表目錄 12 第1章 研究動機與目的 14 1-1 前言 14 1-2 建築生命週期日常使用階段之概述及碳排 16 1-3 研究動機與目的 17 1-4 研究流程 17 第2章 辦公建築樣本之建立 21 2-1 蒙地卡羅法用於建築相關研究之回顧 21 2-2 蒙地卡羅法及拉丁超立方抽樣(LHS)建立設計案例之方法 22 2-3 建築組成因子之建立 23 2-3-1 建築因子 24 2-3-2 使用因子 26 2-3-3 空調因子 33 2-4 使用者輸入因子與ENVLOAD計算流程概說 35 2-5 建築樣本之ENVLOAD表現 39 第3章 辦公建築日常使用階段耗能之計算 41 3-1 Energy Plus 能耗分析軟體與建築組成因子之結合 41 3-2 其他日常使用階段之碳排量推估 46 3-2-1 換氣設備耗能計算 46 3-2-2 照明耗能 50 3-2-3 給排水系統耗能 51 3-2-4 電梯設備耗能 53 3-2-5 辦公事務機器耗能 56 3-3 建築樣本之全年日常使用階段耗能與碳排表現 56 第4章 建築碳排評價系統 61 4-1 建築耗能評價系統相關文獻回顧 61 4-1-1 Commercial Building Energy Asset Score 61 4-1-2 Energy Star Score 能源效率評價系統 64 4-1-3 建築耗能評價系統回顧小結 68 4-2 基線建築案例之建立與其耗能表現 68 4-3 建築碳排評價系統之建置方法 72 4-4 建立碳排評價系統建立之結果 73 第5章 類神經網路空調耗能預測模型之建立 82 5-1 類神經網路與建築耗能相關之文獻回顧 82 5-1-1 類神經網路簡介 82 5-1-2 類神經網路與建築耗能預測相關研究 83 5-2 利用倒傳遞類神經網路預測空調耗能用電密度之模型 84 5-2-1 神經網路之輸出因子之考量 84 5-2-2 神經網路之架構 85 5-2-3 神經網路參數之挑選方式 88 5-3 類神經網路之建立結果 89 第6章 建築案例日常使用階段碳排評分系統之使用 94 6-1 建築案例日常使用階段碳排評分之流程 94 6-2 對評分系統之結果之建議 101 第7章 結論與建議 102 7-1 研究結論 102 7-2 後續研究之建議 103 參考資料 105 | |
dc.language.iso | zh-TW | |
dc.title | 辦公建築日常使用階段碳排評價系統之研究 | zh_TW |
dc.title | Research on the carbon emission benchmarking system of office building during usage phase | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃瑞隆,郭柏巖 | |
dc.subject.keyword | 建築碳排,建築生命週期,日常使用階段,建築耗能,碳排評價系統, | zh_TW |
dc.subject.keyword | building carbon emission,building life cycle,building daily usage phase,building energy consumption,carbon emission benchmark system, | en |
dc.relation.page | 107 | |
dc.identifier.doi | 10.6342/NTU201903162 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-13 | |
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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