教育建筑的节能和低碳导向改造框架与天津大学的案例研究
Xinge Du1, Xiang Liu2, Feng Gao3
1School of Architecture, Tianjin University, Tianjin, 300072, China.
Scientific reports
|August 6, 2025
概括
本研究介绍了一种数据驱动的工作流程,以优化教育建筑改造,平衡能源效率和日光. 这种方法成功地减少了能源消耗和碳排放,同时改善了自然光线.
科学领域:
- 建筑科学 建筑科学
- 可持续的建筑 可持续的建筑
- 能源效率 能源效率 能源效率
背景情况:
- 现代教育建筑通常使用高窗与墙的比例进行日照,导致增加太阳能热量增益和能源使用.
- 在教育设施中,迫切需要节能和低碳设计策略,以支持全球可持续发展目标.
研究的目的:
- 开发和验证数据驱动的工作流程,以优化教育建筑的改造设计.
- 为了平衡可持续能源使用,有用的日光照明和减少碳排放的竞争目标.
主要方法:
- 一种混合方法,将遗传算法与基于贝叶斯优化的XGBoost模型相结合,用于多目标优化.
- 使用建筑信息建模 (BIM) 和模拟工具 (犀牛,女工具,WallaceiX) 进行设计和性能分析.
- 使用Python进行数据处理和模型验证,包括与其他机器学习模型 (LGBM,AdaBoost,Random Forest) 的比较.
主要成果:
- 基于贝叶斯优化的XGBoost模型表现出高预测准确度 (0.86准确度,0.816F1得分),优于其他模型.
- 灵敏度分析确定了影响性能的关键参数:面向北的窗户与墙壁的比率和立面/屋顶厚度显著影响日照和能源使用.
- 发现封装材料的选择和厚度是减少全球变暖潜力 (GWP) 的最关键因素.
结论:
- 拟议的数据驱动工作流程有效地优化了教育建筑的改造,以满足低碳目标和日光要求.
- 该研究验证了通过战略包裹设计和遮阳来大幅减少能源消耗和碳排放的潜力.
- 这项研究为提高全球教育建筑的可持续性提供了有价值的框架.
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