| 研究生: |
羅承宗 Lo, Cheng-Zong |
|---|---|
| 論文名稱: |
都市空間結構對住宅建築能源績效之影響 Exploring the Impact of Urban Structure on Residential Building Energy Performance |
| 指導教授: |
趙子元
Chao, Tzu-Yuan |
| 學位類別: |
碩士 Master |
| 系所名稱: |
規劃與設計學院 - 都市計劃學系 Department of Urban Planning |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 107 |
| 中文關鍵詞: | 都市空間結構 、住宅建築能源績效 、節能城市 、都市能源特徵 、能源績效表現權衡 |
| 外文關鍵詞: | urban structure, residential building energy performance, energy-efficient city, urban energy characteristics, energy performance trade-offs |
| 相關次數: | 點閱:465 下載:1 |
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為減緩溫室氣體排放對全球氣候造成之影響,及於本世紀中達成淨零排放目標,各國城市紛紛投入於淨零城市轉型,都市綠能及節能城市等逐漸成為當代規劃領域討論的議題,各式減碳技術開始受到關注並被納入於城市減碳策略中,然而,除了創新技術之應用,都市空間結構本身亦是影響能源供給與消耗的基礎,並決定著各減碳技術所能達到的最大潛力,由此為開端,本研究以「都市空間結構對住宅建築能源績效之影響」為命題,並選定原台南市為研究範圍進行探討。
本研究首先透過文獻回顧建立都市空間結構對住宅建築能源績效之影響途徑,提拱本研究一整體性操作及評估框架,篩選出具有代表性且多元的都市空間結構因子與住宅建築能源績效指標,以使本研究分析內容得以反映出廣泛的都市空間結構特性及不同面向的能源績效表現。在實證分析上,以地理資訊系統(GIS)為主要操作工具,針對研究範圍以村里為單元對各變數進行分析,都市空間結構分析方面運用三維建物圖資、國土利用調查、Landsat 8衛星影像資料等,實現對地表覆蓋、建築屬性、土地單元及土地使用四個面向之分析;住宅建築能源績效分析則將台電非營業用電統計與不同資料進行綜合分析,並建構數值地表模型進行太陽輻射量模擬,以計算出代表著能源消耗、效率與供需平衡的三項能源績效指標數值。
數據分析上透過皮爾森相關分析及多元回歸分析,首先釐清都市空間結構因子對住宅建築能源績效指標之關係,而後再透過分群分析進一步對複雜關係進行探索及檢視,最後輔以過往研究指出之現象進行整合論述,透過上述操作,本研究指認出建築覆蓋程度、建物規模(大小與樓高)及住宅環境商業使用程度三項關鍵性的都市空間結構因子,及針對本研究範圍進行都市能源特徵描述。此外,透過三項住宅能源績效表現之權衡,提出利於整體能源績效表現之都市空間結構特性。
綜上,本研究所建立之都市空間結構與住宅建築能源績效分析框架和操作方法,以及本研究實證成果發現利於能源績效表現之都市空間結構特性,將可作為我國未來推動節能城市及制定空間調適策略之參考依據。
To mitigate the impact of greenhouse gas emissions on global climate and achieve net-zero emissions in the future, cities worldwide are transitioning towards net-zero urban environments. The urban structure itself fundamentally influences energy supply and consumption, determining the maximum potential of each carbon reduction technology. This study aims to explore the impact of urban structure on residential building energy performance to support urban net-zero transitions.
Tainan City was selected as study area due to various urban structures and the study was divided into 2 main parts. In the first stage, Geographic Information System (GIS) and some other tools were applied. Research variables including urban structure factors and residential building energy performance indicators were built out by data processing and spatial analysis. In the next stage, data analysis employs Pearson correlation analysis and multiple regression analysis to clarify the relationships between these factors and indicators. Further, some data was divided into groups or selected based on descriptive statistics to explore and examine complex relationships.
The results identified 3 key urban structure factors: building coverage, building size (area and height), and the extent of commercial use in residential environments. These factors are essential for understanding the urban energy characteristics within the study area. By balancing the 3 energy performance indicators, urban structure characteristics conducive to overall energy performance were proposed as well.
The analytical framework, methodologies and the empirical findings in this study were expected to serve as a reference for promoting energy-efficient cities and formulating spatial adaptation strategies.
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