在绿色建筑中整合机器和深度学习技术,以提高能源效率和环境可持续性
Shahid Mahmood1, Huaping Sun2,3, El-Sayed M El-Kenawy4,5
1School of Finance and Economics, Jiangsu University, Zhenjiang, China. shahidnajam786@live.com.
Scientific reports
|September 2, 2024
概括
机器学习和深度学习模型加速绿色建筑设计. 图形神经网络 (GNN) 和长短期记忆 (LSTM) 模型显示,在可持续建筑中优化资源使用和乘客舒适性方面,其精度更高.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 绿色建筑 (GB) 设计整合了可持续实践,但耗时.
- 先进的人工智能 (AI) 和机器学习 (ML) 可以加速GB设计.
- 优化资源消耗,乘客舒适度和环境影响是GB的主要目标.
研究的目的:
- 使用机器学习和深度学习 (DL) 开发绿色建筑设计的预测模型.
- 为了优化资源消耗,提高乘客舒适度,减少对环境的影响.
- 在GB设计中比较各种ML和DL技术的性能.
主要方法:
- 使用ASHARE-884数据集进行模型培训和评估.
- 执行探索性数据分析 (EDA),包括数据清理,分类和标签编码.
- 应用Z-Score规范化用于数据预处理.
- 实施的ML模型 (RF,DT,极端GB,堆叠) 和DL模型 (GNN,LSTM,RNN).
主要成果:
- 图形神经网络 (GNN) 和长短期记忆 (LSTM) 模型显示出更高的准确性和效率.
- 这些模型在提高环境可持续性方面表现优于传统的DL技术.
- 使用准确性,精度,回忆和F1分数指标来评估性能.
结论:
- GNN和LSTM模型对于加速绿色建筑设计流程是有效的.
- 人工智能和机器学习技术对建筑物实现环境可持续性做出了重大贡献.
- 该研究为绿色建筑设计中的数据驱动优化提供了一个框架.
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