基于主动学习的机器学习方法,以提高绿色建筑能源消耗的环境可持续性
Shahid Mahmood1, Huaping Sun2,3, Amel Ali Alhussan4
1School of Finance and Economics, Jiangsu University, Zhenjiang, China. shahidnajam786@live.com.
Scientific reports
|August 27, 2024
概括
这项研究引入了一种机器学习模型,以优化绿色建筑的能源效率. 预测模型显著降低了能源消耗,提高了建筑物的可持续性和运营性能.
科学领域:
- 可持续的建筑 可持续的建筑
- 建筑能源管理 建筑能源管理
- 机器学习应用 机器学习应用
背景情况:
- 建筑业占全球能源消耗的近40%,这凸显了对能源效率的需求.
- 绿色建筑 (GB) 往往消耗的能源比设计的更多,原因包括居住者行为和能源管理差距等因素.
- 建筑自动化系统 (BAS) 对于提高绿色建筑的能源效率至关重要.
研究的目的:
- 开发用于绿色建筑设计的预测机器学习模型,以尽量减少能源消耗.
- 为了提高室内可持续性和绿色建筑的运营效率.
- 通过先进的建模来解决绿色建筑中的能源性能差距.
主要方法:
- 利用数据集来预测个别的冷却和加热负载.
- 采用数据可视化,Z-Score规范化和数据集分割进行预处理.
- 开发了一个基于主动学习和各种机器学习回归器 (随机森林,梯度提升,XGBoost等) 的模型. ) 的情况.
主要成果:
- 拟议的模型,特别是CBR-AL变体,实现了高精度,R平方值为0.9975用于冷却 (Y1) 和0.9883用于加热 (Y2).
- 在预测冷却和加热的能源消耗方面表现出显著的性能改善.
- 该模型有效地减少了能源消耗,并提高了室内环境质量.
结论:
- 开发的预测模型为优化绿色建筑中的能源管理提供了强大的解决方案.
- 成功实施可以节省大量成本,减少碳足迹,提高运营效率.
- 这项研究为可持续建筑设计和能源管理的预测建模的未来进展奠定了基准.
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