Mohsenpour Majid, Salimi Mohsen, Kermani Atieh, Amidpour Majid
Department of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.
Renewable Energy Research Department, Niroo Research Institute (NRI), Tehran, Iran.
Heliyon. 2024 Dec 31;11(1):e41572. doi: 10.1016/j.heliyon.2024.e41572. eCollection 2025 Jan 15.
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 energy efficiency. However, most studies focus on idealized conditions, neglecting the real-world variability of indoor and outdoor environments. This study proposes a novel machine learning-based ensemble stacking model to predict ventilation rates in passive cooling buildings, addressing the challenges of black-box modeling. The model's performance is improved across key metrics such as R, RMSE, and MAE. For the first time, uncertainty and sensitivity analysis is applied to assess the impact of indoor and outdoor conditions on ventilation rates. Sensitivity analysis shows that the reference model's ventilation rate highly depends on inlet air temperature, internal temperatures at 0.1 and 0.2 m, and internal wall heat flux, with optimization of these parameters having a significant impact on building performance. In contrast, the test building relies on fewer parameters, with external temperature, outlet air temperature, and net roof radiation being notable factors; as ambient temperature increases, so does the ventilation rate. The analysis reveals that uncertainties have minimal impact in the reference building, while the test building demonstrates greater sensitivity during warmer months, emphasizing the importance of accounting for seasonal variations. This research underscores the significance of optimizing key features to enhance natural cooling and ventilation, contributing to sustainable climate control solutions and providing an interpretable, robust model for predicting ventilation rates in energy-efficient buildings.
受气温上升和城市化推动，全球对空调系统的需求不断增加，导致能源消耗和温室气体排放上升。暖通空调系统，尤其是空调，占建筑能源使用的近一半，凸显了高效制冷解决方案的必要性。被动式制冷，尤其是辐射制冷，具有降低制冷负荷和提高能源效率的潜力。然而，大多数研究集中在理想化条件，忽略了室内和室外环境的实际变化。本研究提出一种基于机器学习的新型集成堆叠模型来预测被动式制冷建筑的通风率，以应对黑箱建模的挑战。该模型在诸如R、均方根误差（RMSE）和平均绝对误差（MAE）等关键指标上的性能得到了提升。首次应用不确定性和敏感性分析来评估室内和室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米处的内部温度以及内墙热通量，优化这些参数对建筑性能有显著影响。相比之下，测试建筑依赖的参数较少，外部温度、出气温度和屋顶净辐射是显著因素；随着环境温度升高，通风率也会升高。分析表明，不确定性在参考建筑中的影响最小，而测试建筑在较温暖月份表现出更大的敏感性，强调了考虑季节变化的重要性。本研究强调了优化关键特征以增强自然制冷和通风的重要性，有助于实现可持续的气候控制解决方案，并为预测节能建筑的通风率提供一个可解释的、稳健的模型。
