基于增强型集成学习的新型辐射冷却建筑通风率不确定性和敏感性分析
Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building
中文简介
受气温升高和城市化进程的推动，全球对空调系统的需求不断增长，导致能源消耗和温室气体排放增加。暖通空调系统（尤其是空调）的能耗几乎占建筑总能耗的一半，凸显了高效制冷解决方案的必要性。被动式制冷，特别是辐射制冷，具有降低制冷负荷和提高能源效率的潜力。然而，大多数研究都侧重于理想化条件，忽略了室内外环境的实际差异。本研究提出了一种基于机器学习的新型集成堆叠模型，用于预测被动式制冷建筑的通风率，从而克服了黑箱建模的挑战。该模型在R²、RMSE和MAE等关键指标上均表现出色。本研究首次应用不确定性和敏感性分析来评估室内外条件对通风率的影响。敏感性分析表明，参考模型的通风率高度依赖于进气温度、0.1米和0.2米处的室内温度以及内墙热通量，优化这些参数对建筑性能具有显著影响。相比之下，测试建筑所依赖的参数较少，其中外部温度、出风口空气温度和屋顶净辐射是几个显著因素；随着环境温度升高，通风率也随之增加。分
英文摘要（原文）
The rising global demand for air conditioning systems, driven by increasing temperatures and urbanization, has led to higher energy consumption and greenhouse gas emissions. HVAC systems, particularly AC, account for nearly half of building energy use, highlighting the need for efficient cooling solutions. Passive cooling, especially radiative cooling, offers potential to reduce cooling loads and improve
题录（GB/T 7714）
Mohsenpour M, Salimi M, Kermani A, 等. Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building[J]. Heliyon, 2025, 11(1): e41572. DOI: 10.1016/j.heliyon.2024.e41572.
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