建築中的日光對於室內人員的舒適性、健康，以及建築物能源消耗有重要的影響，因此早期階段的日光設計決策對於建築的整體永續性發展至關重要。然而，在建築早期設計階段中實現日光評估仍面臨著許多挑戰。例如，過去經常採用的日光模擬評估方法計算量龐大並且十分耗時。為了在設計早期階段快速獲取有關建築日光性能的資訊以輔助決策，最近的研究開始使用機器學習、建築最佳化演算等方法來改善日光評估流程。透過機器學習可以開發日光預測模型快速進行評估，然而目前的模型訓練方法極大地限制了日光模型的應用範圍。建築最佳化演算可以在眾多設計備選方案中快速探索最佳解決方案，而其過程仍需大量計算且時間成本高昂。
為了提高建築實務進行日光評估的可行性，本研究以辦公建築立面設計為研究對象。首先，本研究以不同的機器學習方法開發了一種創新的日光預測模型，這些模型透過中介特徵進行訓練，使模型可以不受設計參數的限制而擴展模型的應用範圍。接著，將預測模型整合至最佳化流程中，進行了包含採光性能、能源消耗和使用者熱舒適的多目的最佳化評估，並演示了決策流程。整體而言，整合了參數化平台、機器學習和最佳化演算法等技術，探討快速進行評估、設計最佳化和簡化預測流程的可能性。
在開發日光預測模型方面，結果顯示分析表面上的DA和ASE小時值都得到了良好的預測，成功再現了不同立面的日光分佈情形，表明了採用中介特徵的模型轉譯訓練方法對於擴展應用範圍的有效性。不同的學習方法中，以RF和GBDT所訓練之模型有最佳的預測表現。此外，使用日光預測模型可以節省約90%的評估時間。在最佳化評估方面，結果顯示，Pareto解決方案包含了三個性能指標權衡後的結果，設計人員可以在可視化決策流程中輕易的比較最佳方案的性能和設計樣式。而整合預測模型的最佳化流程能夠在一日內完成評估，展現了可觀的時間優勢和可行性。為了實現更好的軟體相容性，本研究也開發並簡化了流程，設計人員只須導入設計模型並定義少量參數即能進行日光預測，使設計和評估之流程及工具相容性大為提升。整體而言，雖然預測模型至今仍存在一些限制，目前的研究成果對於透過機器學習模型快速預測、擴展模型應用範圍的方法、快速最佳化評估和設計決策方法，已經取得了重大進展。

In order to obtain information about building daylighting performance earlier in the design stage, recent studies have begun to develop daylight prediction models using machine learning methods. Most studies have adopted design variation parameters as the input parameters for model training, but this method greatly limits the scope of application of the daylight model.
By extending application to different design possibilities, this research proposes a novel daylight model, which includes a pre-processing procedure to convert the design model into “Intermediary features” as input parameters representing daylight penetration performance. By changing geometry of one kind of parametric façade and performing daylighting simulation, we were able to generate the data for training, including Intermediary features and sDA/ASE values. The daylight model was then trained using an artificial neural network. Finally, the proposed daylight model was tested by predicting the daylight performance of other kinds of façades.
The results indicated that the value of DA and ASE hours per grid were well predicted and that the daylight distribution was reproduced even with different kinds of façades. The deviations of sDA and ASE from simulation results ranged from 1.7 to 6.1% and 0.3 to 2.1 hours respectively. The reproducibility, the predictive capability, and most importantly the extension of model applicability were all demonstrated for the proposed model. Furthermore, comparing to the daylighting simulation, the method using the proposed daylight model is estimated to save 9/10 daylighting evaluation time. This is critical for implementing the evaluation in the early design stage.

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