被動式設計已然是當代建築的設計趨勢。對於台灣的氣候條件而言，控制進入室內的太陽輻射是永續建築設計中很重要的一環，透過立面遮陽設計可以大大減少空調能源消耗並提升使用者的熱舒適。但目前設計的初期流程中加入熱舒適評估仍面臨時間成本高昂、技術困難與模擬軟體限制等諸多問題。
為了將熱舒適評估導入早期設計流程以輔助決策，本研究提出「立面轉譯器」的工作流程，透過綜合多個模擬軟體功能，達成在參數化設計的條件之下模擬空間中的熱舒適情形目標。本研究以辦公室做為模擬的空間，並考量風速與濕度條件以作用溫度（OT）作為衡量基準。此外，根據台灣的熱舒適區間範圍提出了年度不舒適小時數（ATDHs）與空間熱舒適自主度（sTCA）的評估指標。
機器學習方法已應用在能源、採光等領域。本研究為了提高建築事務進行評估的可能性，在成功建立了模擬流程之後，應用「預處理器」的概念進行機器學習。預處理器的方法將格點資訊轉換為中介特徵輸入人工神經網絡(Artificial Neutral Network)的模型，透過多次變化基本模型參數重複模擬取得共60,000筆資料進行訓練。隨後，調整機器學習模型之超參數與不同輸入特徵組合建立預測模型。表現最良好之模型Dodo_1在驗證集有RMSE=2.42、R2=0.91的預測性能，模型更可同時適用於不同幾何類型之立面，證明中介特徵之概念同樣適用於熱舒適模型訓練上。
最後，引用實際落成之立面設計理念對一位於台北之辦公室進行案例操作。透過Wallacie模組，於目標空間作全年熱舒適、視覺開闊度與全年空調熱負荷的多目的最佳化。經過300個方案的探索找出性能表現良好之方案作可視化，設計者可輕易的透過流程比較最佳方案的目標性能和設計樣式。
透過本研究，整合了參數化設計中熱舒適模擬之多種工具，擴大了模擬之相容性，並進一步提出機器學習之模型，提供設計者於建築設計前期對多種方案的評估機制，且省去大量操作學習成本與80% 模擬時間的耗費，大幅提升了熱舒適評估於設計階段被採納的可能性。

Passive design architecture has become the trend in architecture design. For subtropical climate like Taiwan, controlling incoming solar radiation to a building is one of the main targets of sustainable architecture designers because it decreases HVAC energy consumption and maximizes thermal comfort and usable daylight.
To assess thermal comfort, this research introduced a new framework using the Rhinoceros platform to simulate radiant discomfort across spaces with various types of parametric façades. The framework was established based on the ASHRAE55 appendix and improved the longwave MRT calculation by using the Radiance-based pre-processing method. The use of an operative temperature (OT) map was then highlighted as a measure of the combined effect of mean radiant and air temperatures considering the conditions of air velocity and relative humidity in office layouts. Furthermore, the metric of Annual Thermal Discomfort Hours (ATDHs) is proposed using local thermal comfort range.
The concept of “pre-processor” was then applied to in the machine learning data collection. Grids data were pre-processed that convert the design model into “Intermediary features” as input parameters representing ATDHs performance. The thermal comfort model was then trained using an artificial neural network. Consequently, the proposed thermal comfort model was tested by predicting the performance of other kinds of façades.
Finally, this study developed a process that integrates machine learning predictive model and multi-objective algorithms to achieve rapid evaluation as well as obtain optimal solutions. Using façade design as a case study, we demonstrate the design decision process with regard to the optimized solutions.

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