近年來，隨著能源緊缺、溫室效應的加劇，建築碳排放等議題在國際上受到廣泛關注。建築在設計前期的最佳化可以大幅度優化其性能，也吸引了許多研究者的目光，如降低建築碳排放、投資額、室內舒適度的影響。但是許多研究中的建築性能最佳化策略沒有顧及一些實際情況，無法高效運行。本研究提出了一種新的建築性能最佳化整合設計策略來探究目標建築的碳排放、生命週期成本與熱舒適性最佳方案。本研究的最佳化策略可總結為四個步驟：第一，首先通過五種採樣方法對選取的建築輸入參數進行採樣，並將其輸入到建築模型中進行模擬。然後，應用多種敏感度方法評估各特徵對建築碳排放、投資額、室內舒適度的綜合貢獻率。最後，排除綜合貢獻率在最後5%的非重要參數。第二，將過濾後的參數導入集成學習模型，探索採樣法、超參數搜索法和超參數數值的最佳組合方案，以達到最高的預測效能。第三，將最佳的集成學習模型分別與三種最佳化算法 (NSGA-II、NSGA-III、C-TAEA) 相結合，使用超體積指數進行評估，選擇出性能最好的最佳化算法。第四，根據提出的新公式選擇最佳建築方案，並且與傳統的烏托邦方法選擇的方案進行對比，體現了新策略的優勢。
研究結果表明，將輸入參數評估和篩選、多採樣方法比較和超參數優化，集成學習模型結合可以實現高精度預測效能。R2值可達0.980，MAE值為31.20，RMSE值為56.71。同時，C-TAEA的優化效能和收斂程度均高於NSGA-II和NSGA-III，超體積值達到了0.721，但它是近兩年提出的新算法，目前尚未被應用至建築性能最佳化領域。其次，與傳統方法相比，最佳化策略可以顯著提高建築最佳化流程的計算速度。本研究提出的最佳平衡選擇法考慮了三個目標的優化，不僅解決了不同目標的不同優化範圍的問題，而且使優化效果最大化。與傳統的烏托邦解決方案相比，最佳平衡選擇法兼顧了每個目標的帕累托平均值和可優化幅度。同時，它新增了最佳化值，平衡了最佳化目標，也使三個目標最佳化的數值增加到最大。最佳平衡選擇法使最終方案的各個結果的效能更加均衡合理，優於傳統的烏托邦解。最後，該方案降低了34.7%的全生命週期碳排放、13.9%的全生命週期投資額和26.6%的室內不舒適小時數，結果更為合理。最終證明，這個建築性能最佳化策略可以高效地優化建築目標，產生更為平衡的最佳建築方案，可在建築性能最佳化領域推廣。

Recently, with the intensification of the energy crisis and greenhouse effect, carbon emissions and other issues have attracted a lot of attention all over the world. In the early architectural design stage, performance optimization has been subject to much research, such as the impact of building carbon emission, life cycle cost, and thermal comfort. This study proposed a new integrated design strategy for building performance optimization to explore the best building scheme for carbon emission, life cycle cost, and thermal comfort of the target building. The strategy of this study mainly includes four steps. Firstly, five sampling methods were used to sample the selected features and input them into the building model for simulation. Then, various sensitivity analysis methods were applied to assess the comprehensive contribution rate of features to building carbon emission, life cycle cost, and indoor comfort. At last, the unimportant features with minimum contribution rates were excluded. Secondly, the filtered parameters were imported into the ensemble learning model for evaluation, exploring the best combination of sampling method, hyperparameter search method, and value to obtain the best prediction efficiency. Thirdly, the optimal ensemble learning model was combined with three optimization algorithms (NSGA-II, NSGA-III, and C-TAEA) respectively. The hypervolume index was used to evaluate and select the optimization algorithm with the optimal performance. Fourthly, the best building scheme was chosen according to the proposed new formula and compared with the scheme selected by the traditional Utopian method, reflecting the new strategy's advantages. This study proved that the research strategy was an effective method to reduce building carbon emissions, life cycle costs, and indoor comfort.
The results showed that the ensemble learning model could achieve high-precision prediction efficiency by combining input parameter evaluation and screening, multi-sampling method comparison, and hyperparameter optimization. R2 value can reach 0.980, MAE value was 31.20, and RMSE value was 56.71. Meanwhile, the optimization efficiency and convergence degree of the Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) were higher than the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), and the hypervolume value reached 0.721. However, as a new algorithm recently proposed, it had not been applied in building performance optimization. Secondly, the optimization strategy can effectively improve building efficiency. Compared with the traditional methods, the calculation speed was significantly improved. The OBS proposed in this study considered the optimization of three objectives, which solved the problem of different optimization ranges of different objectives and minimized the optimization value. Compared with the traditional Utopian solution, the optimal balance selection method considered both the Pareto average value and variation range of each goal. It balanced the optimization objectives and maximized the optimization values of the three objectives. The OBS method made the efficiency of each result of the final scheme more balanced and reasonable, which was better than the traditional Utopian solution. Finally, the scheme reduced the life cycle carbon emission by 34.7%, the life cycle cost by 13.9%, and the indoor discomfort hours by 26.6%, which can be widely used in building performance optimization. Therefore, this building performance optimization strategy can efficiently optimize the building objectives and produce a more balanced and optimal building scheme.

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