都市微氣候改善策略對辦公建築耗能影響之研究

Abstract

隨著都市規模不斷擴張，都市熱島效應日益加劇，使得建築空調耗能增加，本研究以改善都市微氣候為目標，整合ENVI-met都市微氣候模擬軟體與EnergyPlus建築能耗軟體，藉由都市街谷設計的策略，降低建築的空調耗能。 為量化設計策略對於微氣候改善的效益，回顧過去微氣候改善相關研究，本研究提出的微氣候改善因子為：建築與街道的高寬比、壁面反射率、行道樹綠覆率、壁面垂直綠化覆蓋率。此研究以台北TMY3標準氣象年作為輸入氣象條件，以ENVI-met模擬改善後的街谷微氣候，利用田口實驗計畫法模擬11種因子配置的情境，得到模擬後的微氣候資料後利用Meteonorm全球氣象資料庫，將短期資料生成長期的微氣候氣象資料，作為EnergyPlus建築耗能模擬輸入的氣象條件，得到微氣候改善後的全年建築空調耗能結果。 將全年建築空調耗能除以樓地板面積得到全年建築空調耗能密度，將11種街谷情境的全年建築空調耗能密度進行變異數分析，發現各因子的貢獻度分別為:行道樹綠覆率35%、街道高寬比29.7%、壁面綠化覆蓋率23.4%、壁面反射率3.6%，其中行道樹綠覆率、街道高寬比、壁面綠化覆蓋率為顯著的因子。研究結果顯示深街谷、壁面反射率0.7、行道樹綠覆率100%、壁面垂直綠化覆蓋率100%的街谷有最低的全年空調耗能密度139.6 kwh/m^2，最後整理出微氣候因子效果對於全年空調耗能密度與全年節能率的關係式，讓使用者可以輸入因子改善程度得到全年空調耗能密度與全年節能率，作為未來都市規劃的參考。

The urban heat island effect is increasingly intensified, which makes the building cooling energy demand become greater. Urban greening is an effective strategy to improve the urban thermal environment. This study aims to improve the urban microclimate and integrate building energy consumption software EnergyPlus. In order to quantify the benefits of the design strategies for microclimate improvement. The microclimate improvement factors in this study are building height to width ratio, wall reflectance, trees coverage rate, wall surface green coverage rate. In this study, Taipei TMY3 was used as the input meteorological conditions, ENVI-met was used to simulate the improved cases microclimate, and the Taguchi experimental planning method was used to configure the factors of 11 simulated scenarios. Obtaining the annual building cooling energy demand, and analyze the variance of the annual building cooling energy demand in 11 street scenarios. The contribution of each factor is: tree coverage rate is 35%, the H/W ratio is 29.7%, the wall green coverage rate is 23.4%, the wall reflectance is 3.6%. The tree coverage rate, H/W ratio, wall green coverage rate are significant factors. The results show that the deep valley, the wall reflectance of 0.7, the tree coverage rate of 100%, and the wall green coverage rate of 100% has the lowest annual building cooling energy demand density of 139.6 kwh/m^2. The effect of factors on the annual building cooling energy demand density and the annual energy saving rate allows users to input the degree of improvement of the factor to obtain the annual building cooling energy demand density and the annual energy saving rate, as a reference for urban planning.