一个可解释的机器学习框架,用于实现多目标的碳减排,针对建筑改造中的物质运营季节性排放
Huai Zhu1, Chenghai Hu2, Congyue Zhou3
1School of Arts Design, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China.
Scientific reports
|December 1, 2025
概括
本研究提出了一个使用可解释的人工智能和低碳建筑改造优化的框架. 它实现了物质和运营碳排放的显著减少,提供了碳中和的实际策略.
科学领域:
- 建筑科学与工程 建筑科学与工程
- 可持续建筑 可持续建筑
- 建筑中的人工智能
背景情况:
- 建筑行业对碳中和的努力需要有效的现有建筑改造战略.
- 低碳改造是缓解建筑环境中的气候变化影响的关键途径.
研究的目的:
- 开发一个系统的框架,整合可解释的机器学习和多目标优化,以优化建筑装修项目的碳排放.
- 确定影响碳排放的关键设计变量,并通过案例研究验证框架.
主要方法:
- 基于高分辨率的碳排放模拟数据,使用6个机器学习算法 (CatBoost显示优异性能) 开发预测模型.
- 使用SHAP值来确定影响碳排放的关键设计变量 (例如FLH,WWR1,NOF,WWR2).
- 在三层工业建筑改造的案例研究中,应用NSGA-II算法进行多目标优化.
主要成果:
- CatBoost模型在预测碳排放方面表现出了卓越的表现.
- 确定了影响碳排放的关键设计变量,为决策提供了基于证据的基础.
- 多目标优化产生了四个不同的可行解决方案,其中最平衡的解决方案实现了物质碳排放强度 (MCEI) 的71.06%降低,运营碳排放强度 (OCEI) 的37.20%降低,以及季节性碳排放平衡 (SCEB) 的24.75%改善.
结论:
- 拟议的框架为低碳建筑改造提供了一个系统,可扩展和可复制的技术途径.
- 建议的低碳策略包括材料再利用,采用低碳材料,隔墙优化,增强建筑外的热性能和大厅设计改进.
- 该研究为实现建筑行业碳中和目标提供了显著的实际价值.
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