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系統識別號 U0026-1007201919260800
論文名稱(中文) 建構都市規模下的微氣候、住宅能源需求及熱風險空間分佈地圖的開發研究
論文名稱(英文) Development on constructing the spatial distribution map of microclimate, energy demand and thermal risk of residential buildings at city scale
校院名稱 成功大學
系所名稱(中) 建築學系
系所名稱(英) Department of Architecture
學年度 107
學期 2
出版年 108
研究生(中文) 林奉怡
研究生(英文) Feng-Yi Lin
學號 N78041056
學位類別 博士
語文別 中文
論文頁數 122頁
口試委員 指導教授-林子平
召集委員-黃瑞隆
口試委員-張烔堡
口試委員-黃國倉
口試委員-蔡耀賢
中文關鍵字 都市微氣候  都市熱島  熱風險  空調能耗 
英文關鍵字 Urban microclimate  urban heat island  thermal risk  air conditioning energy consumption  Morphing approach 
學科別分類
中文摘要 隨著都市規模擴大和多樣化發展,通常位於郊區的單一的氣象站資料已不足以代表及反應整個都市的微氣候變化,這也導致在模擬建築能耗上易有估算錯誤之情況發生。本研究選擇在台灣台南市設置34個自動氣象測站,進行1年的溫溼度實測。採用Morphing approach,結合從GIS資料可取得的都市環境參數來構建都市逐時的局部微氣候資料。討論使用6種周圍環域來決定UHI作用下都市局部逐時溫度及相對溼度的適宜性。誤差分析結果顯示以1000×1000 m2,分成內外層和上下風區的周圍環域,環境參數為綠地水域面積、建築面積及道路面積之情況下可得到最小的預測誤差。
根據生成之全都市逐時氣象資料繪製都市熱島效應(UHII)地圖,同時利用EnergyPlus模擬不同微氣候下的透天住宅能耗。此外,結合能源模擬與地理資訊,建立以都市住商混合住宅EUI 參考值、建築資料以及人口資料做輸入參數來預測都市網格總能耗的簡單方程式。並以UHII為指標探討都市熱應力(CHS)、自然通風可利用率、冷房度日、空調能耗、熱風險及都市規劃的關係。結果顯示台南5-10月的平均溫度落在27.5-30°C,11-4月則在20.5-23°C。熱季與涼季RH皆在74%-92%。郊區全年UHII<5000°C-hour,市區則在5000-15000°C-hour。有31%的網格在6月會有熱風險情況發生,另有0.7%的網格在6月與10月皆有熱風險。市區的CHS會隨著UHII的提高,最多會增加9.5%,故自然通風可利用率只有60-70%,冷房需求更是郊區的1.5-2.5倍,故空調EUI最少會多7kWh/m2·year,最多增加19 kWh/m2·year,即每1m2空調電費每年最少比郊區多17.5元,最多增加47.5元。UHII不僅影響建築能耗,UHII也會隨著都市建築總能耗量的提升而增強,但當能耗量超過2500 MWh時,累積UHII的增長趨近於6900°C-hour。當都市內部有大面積或大量的公園綠地或水域時,可有效舒緩熱島效應,自然通風可利用率可升至65-75%,CDD可減少約100°C-day。
英文摘要 As the city expands, the single weather station data is insufficient to represent and reflect the microclimate changes in the entire city. This study measured the temperature and humidity of Tainan for one year. The Morphing approach is combined with GIS data to construct local microclimate data. Use the generated weather data to map the bioclimatic maps, while using EnergyPlus to simulate the energy consumption of the diaspora in different microclimates. In addition, made a model for predicting the total energy consumption of urban grids using EUI, building and population data. Finally, UHII is used as an indicator to explore the relationship between CHS, NV, CDD, air conditioning energy consumption, thermal risk and urban planning. The error analysis results show that the best buffer is dividing 1000×1000 m2 into the outer and inner layer with the upper and lower wind regions. The annual UHII <5000 °C-hour in the suburbs and 5000-15000 °C-hour in the urban area. There will be a thermal risk in June and October. The CHS in the urban area will increase with the UHII, and the CDD will be 1.5-2.5 times that of the suburbs. Therefore, the air-conditioning EUI is increased by at least 7kWh/m2 ·year, and the maximum increase is 19 kWh/m2·year. In addition, UHII will increase with the total energy consumption of urban buildings, when the energy consumption exceeds 2500 MWh, the UHII growth will approach 6900 °C-hour. When there is a large area or a large number of parks or waters in the city, the UHII can be effectively relieved, the NV availability can be increased to 65-75%, and the CDD can be reduced by about 100 °C-day.
論文目次 第一章、 緒論 1
第一節、 研究背景與動機 1
第二節、 文獻回顧 2
第三節、 研究目的與方法流程 7
第二章、 微氣候地圖 9
第一節、 研究範圍與網格尺度 9
第二節、 都市微氣候量測 10
第三節、 局部氣候資料生成 14
第四節、 迴歸模型與誤差分析 21
第五節、 最佳氣候要素預測模型 28
第三章、 生物氣候地圖 31
第一節、 都市微氣候地圖 31
第二節、 都市熱島效應 39
第三節、 熱應力 43
第四節、 冷房度日 46
第四章、 住宅能源與熱風險 49
第一節、 建築模型與模擬工具 49
第二節、 自然通風潛力 54
第三節、 熱風險模型 60
第四節、 住宅空調能耗地圖 66
第五章、 都市能耗地圖 71
第一節、 能源資料的拆分與預測模型建立方法 71
第二節、 網格EUI預測模型結果 75
第三節、 都市總能耗地圖 77
第六章、 微氣候的影響 81
第一節、 熱應力與UHII的關係 81
第二節、 冷房度日與UHII的關係 82
第三節、 熱風險與UHII關係 83
第四節、 自然通風與UHII關係 84
第五節、 冷氣能耗與UHII的關係 85
第六節、 實際住宅用電對UHI影響分析 86
第七節、 都市規劃對UHI的影響 87
第七章、 結論與建議 91
第一節、 結論 91
第二節、 建議 92
參考文獻 95
附錄 發表之期刊 103
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