城市佔了全球1/2以上的人口、2/3以上的經濟流動和3/4的碳排放，改善都市發展遇到的問題相當於積極影響全球50%以上的人口。本研究欲評估及預測城市的建築能源分布與強度，以利建立地區能源政策，改善城市的建築能源性能。
本研究建立了一個用於評估全台各村里建築能源消耗的模型，以建築土地利用、各類建築使用強度與建築空間能耗密度建模，自下而上快速地估算台灣村里建築電力耗用的分布、強度。相較同樣是由下而上的城市建築物理性質建模法，省去每個原型建築物的詳細變量。不僅加快了模擬速度，也擴大了模擬的範圍。最重要的是，模型的時-空單位降為四季日夜-村里，比年-城市更精細、更適合用於台灣，可提供地方團隊建立地方性能源政策時參考。
模型以住宅類建築做初步模擬，並以住宅建築實際用電資料作為驗證對照進行簡單線性迴歸。住宅類建築能耗估算結果級距為0~195(kWh/m2-yr)、台電2018住宅售電數值級距為0~202(kWh/m2-yr)。迴歸結果: R2皆>0.75; RMSE為3~37.39; MAE為1.51~5.03，且p值<0.05，代表本研究能源模型估算值可解釋實際用電值。對於各城市村里住宅能耗高估、低估情形，以估算值xi代入迴歸式ŷ = α + βxi修正，得出的配適值ŷ更接近於實際用電值yi。驗證模型後，將所有類型建築皆代入此模型，所得數據即代表近幾年全台各村里的建築電力消耗情況，並以數據建立全台建築能源耗用地圖。後續用此模型比對城市季節氣溫與季節建築能耗的相關性以探討城市建築能耗和城市熱島關聯、以及預測全台國中小學校建築新增空調量。
研究結果為1.建立村里建築能源模型2.全台各村里建築能耗估算數據及地圖 3.氣溫與建築能耗的關係。其中，台中市於夏季白天12點時建築能耗與都市熱島現象相關性最高4.全台國中小建築新增空調能耗量預測。

Building energy model can evaluate wide range energy demand so as to supply energy strategies. This study developed a model to estimate buildings energy consumption in villages scale. Method based on generalized large-scale geographic information system (GIS), using building land use, building use intensity, and building space energy density modeling.
Compared with the same bottom-up mode of urban buildings energy, the detailed variables of each prototype building are omitted. It not only speeds up the simulation speed, but also expands the scope of the simulation. Most importantly, the time-space unit of the model is reduced to the four seasons day and night-the village, which is more refined than the year-the city, and is more suitable for use in Taiwan, which can provide a reference for local teams to establish local energy policies.
The model took residential buildings for a preliminary simulation, and used electricity sales data of residential buildings as a verification control to perform simple linear regression. Residential building energy consumption estimation results range from 0 to 195 (kWh/m2-yr), and Taipower 2018 residential electricity sales range from 0 to 202 (kWh/m2-yr). Via regression, R2 were all above 0.75; RMSE was 3~37.39; MAE was 1.51~5.03, and p value was lower than 0.05.
After verifying the model, all kinds of buildings were substituted into this model, and the result was used to build a building energy consumption map. More often, this model also used to compare the correlation between Taichung citys’ seasonal temperature and building energy consumption, and predicted electricity use of air-conditioning of primary and secondary schools.

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