通过图形注意力网络和堆叠合体学习来增强智能HVAC优化,这是深Qianhai智能社区的推系统方法
Yuan He1, Ali B M Ali2, Saman Ahmad Aminian3
1School of Accounting, Xijing University, Xi'an Shaanxi, 710123, China.
Scientific reports
|February 11, 2025
概括
这项研究介绍了智能社区的智能HVAC优化系统,使用机器学习来减少15%的能源使用. 先进的系统通过实时环境控制来提高乘客的满意度.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 能源系统 能源系统
背景情况:
- 智能社区需要高效的能源管理系统.
- 传统的空调系统往往缺乏实时适应性.
- 在保持乘客舒适的同时优化能源消耗是关键的挑战.
研究的目的:
- 为深海智能社区设计和实施智能HVAC优化系统.
- 利用先进的机器学习技术提高能源效率和乘客满意度.
- 为智能城市能源管理验证一个可扩展的模型.
主要方法:
- 利用图表注意网络 (GAT) 和堆叠集体学习 (渐变增强机器,神经网络,随机森林).
- 实施了全面的传感器网络,用于实时监测环境参数.
- 采用Z-score规范化,特征工程和Pearson相关系数用于图形构造和数据预处理.
主要成果:
- 使用堆叠组合模型实现了0.93的高曲线下面积 (AUC).
- 在部署后,能源消耗减少了15%.
- 报告说,由于响应性环境控制,乘客满意度有所增加.
结论:
- 智能HVAC优化系统有效降低能源消耗,提高乘客舒适度.
- GAT和集体学习方法在现有系统上显示了显著的优势.
- 经过验证的方法提供了一个可扩展的模型,用于智能城市环境中的能源优化.
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