基于集体学习的增强不确定性和灵敏度分析,用于新型辐射冷却建筑的通风率
Majid Mohsenpour1, Mohsen Salimi2, Atieh Kermani1
1Department of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.
Heliyon
|January 24, 2025
概括
这项研究引入了一种机器学习模型,用于预测被动冷却建筑物的通风率,从而提高能源效率. 它分析了影响通风的关键因素,为可持续的气候控制提供了见解.
科学领域:
- 建筑科学 建筑科学
- 能源效率 能源效率 能源效率
- 机器学习 机器学习
背景情况:
- 全球对空调的需求增加了能源消耗和排放.
- 暖通空调系统,特别是AC,是建筑物中主要的能源消耗者.
- 被动冷却提供了一个可持续的替代方案,但现实世界的变化性往往被忽视.
研究的目的:
- 开发一种机器学习模型,用于预测被动冷却建筑物的通风率.
- 在这个领域解决与黑子建模相关的挑战.
- 为节能建筑设计提供一个可解释和强大的模型.
主要方法:
- 开发了一个集体堆叠机器学习模型.
- 该模型预测在变化的室内和室外条件下通风率.
- 进行了不确定性和灵敏度分析,以评估参数影响.
主要成果:
- 该模型在关键指标 (R2,RMSE,MAE) 中表现得更好.
- 灵敏度分析确定了影响不同类型建筑物通风率的关键参数.
- 不确定性分析显示了建筑物特定的敏感性,特别是在温暖的月份.
结论:
- 优化温度和热流等关键参数显著影响建筑性能.
- 考虑季节性变化和参数不确定性对于准确的预测至关重要.
- 开发的模型有助于可持续的气候控制和节能建筑解决方案.
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